University of Tabriz Journal of Agricultural Mechanization 2383-126X 10 4 2025 12 22 Starting the Header, Threshing and Discharge the Combine Harvester by Pneumatic Jack Starting the Header, Threshing and Discharge the Combine Harvester by Pneumatic Jack 1 13 20987 10.22034/jam.2025.69177.1342 FA Esmaeil Yremtaghlu Department of Biosystem, Faculty of Agriculture, Bu-Ali Sina University , Hamedan, Iran Mojtaba Jaberimoeaz Department of Biosystem, Faculty of Agriculture, Bu-Ali Sina University , Hamedan, Iran Journal Article 2025 09 20 Introduction Agricultural mechanization has become a crucial component in addressing the increasing global demand for food production while minimizing production costs and environmental impacts. Among agricultural machinery, combine harvesters play a vital role by integrating multiple harvesting processes such as cutting, threshing, separating, and unloading into a single operation. However, most conventional combine harvesters rely on hydraulic and mechanical actuation systems for controlling their primary functional units, including the header, threshing, and unloading mechanisms. Although these systems are well established, they are often characterized by relatively high energy consumption, complicated maintenance requirements, the risks of hydraulic oil leakage, and performance degradation under prolonged field use. In recent years, pneumatic actuation systems have gained attention in various agricultural and industrial applications due to their advantages, including simpler design, lower weight, faster response time, no risk of oil leakage, and reduced operational costs. Studies such as those by Johnson (2023) and Gryboś (2024) have reported significant energy-saving potentials for pneumatic systems compared to conventional drives in different industrial settings. However, the use of pneumatic systems in combine harvesters has been limited, and there is a lack of comprehensive research evaluating their performance under real field conditions, particularly regarding durability, energy efficiency, and operational reliability. This study was conducted to design, develop, and evaluate a pneumatic actuator (air cylinder) system as a substitute for traditional hydraulic and mechanical systems in combine harvesters. The main objectives were: To reduce energy consumption and operational time during harvesting. To improve the reliability, uniformity, and responsiveness of the drive system. To assess the durability and maintenance costs under real working conditions. To evaluate the economic feasibility of implementing pneumatic actuation in grain harvesters. Materials and Methods The study was carried out on a New Iran model straw combine harvester manufactured by Sabz Abad Hegmataneh Company in Hamedan Province, Iran. The field experiments were conducted on the Ehsan wheat variety. The research followed a multi-stage approach comprising conceptual design, 3D modeling, dynamic simulation, prototype development, and field testing. Initially, a complete 3D model of the pneumatic actuation system was developed in SolidWorks 2018. Dynamic simulations, including analyses of displacement, velocity, acceleration, and force analysis under varying loads, were performed using MSC ADAMS. The prototype was then integrated into the combine harvester to replace the conventional hydraulic and mechanical drives of three key units: The header unit for height adjustment,The threshing unit for concave clearance control,The unloading system for operating the auger pipe and grain discharge. The experimental design was a randomized complete block design (RCBD) with three replications and three treatments: pneumatic, hydraulic, and mechanical drive systems. Key performance indicators included: Energy consumption (kWh) measured using flow and pressure sensors,Unloading time (s) recorded using a stopwatch,Threshing efficiency (%) and uniformity of power transmission measured under field conditions,Durability (h) tested under dusty and humid environments,An economic evaluation of initial and annual maintenance costs. Statistical analyses were performed using IBM SPSS Statistics 26, employing independent t-tests and one-way ANOVA followed by Duncan’s multiple range tests at a 5% significance level. Results and Discussion The experimental results revealed that the pneumatic system significantly outperformed the conventional hydraulic and mechanical systems across all major performance parameters. Energy Consumption The pneumatic system consumed only 12.3 ± 0.8 kWh, representing a 23% reduction compared to the hydraulic (15.9 ± 1.1 kWh) and mechanical systems (16.4 ± 1.1 kWh). Similar energy-saving benefits were reported by Boyko and Weber (2024) in industrial pneumatic drives, indicating that air-actuated systems inherently require less energy due to the absence of continuous fluid pumping losses typical in hydraulic circuits. Unloading Time The unloading time of the grain tank decreased significantly from 54.3 ± 2.0 s (hydraulic) and 55.1 ± 2.1 s (mechanical) to 39.2 ± 1.5 s for the pneumatic system—a 28% reduction. Faster unloading allows for reduced combine downtime and improved field capacity, which is crucial for large-scale grain production systems. Threshing Efficiency and Power Transmission Uniformity Threshing efficiency reached 91.0 ± 2.2% with the pneumatic system, compared to 84.5 ± 2.5% for the hydraulic and 82.3 ± 0.3% for the mechanical systems. The smoother motion of pneumatic actuators minimized vibrations and shocks, reducing grain breakage and ensuring more uniform power delivery to the threshing drum. Durability and Reliability Durability testing under harsh field conditions (dust, moisture, and variable loads) showed that the pneumatic system maintained stable performance for 1200 operational hours, while the hydraulic and mechanical systems deteriorated after 930 h and 850 h, respectively. Reduced wear and the absence of hydraulic oil contamination were key contributing factors to the extended lifespan. Economic Evaluation The initial cost of the pneumatic system was 17% lower than that of the hydraulic system, while annual maintenance costs were reduced by 35%. The absence of hydraulic fluids, filters, and frequent servicing requirements resulted in significant long-term cost savings, making the pneumatic system economically attractive for farmers. Effect of Field Variables ANOVA results indicated that grain moisture content, threshing drum speed, and combine forward speed significantly influenced grain losses (p < 0.05). However, under optimized operational settings, the pneumatic system consistently exhibited lower grain loss and better performance than conventional systems. Simulation Insights Dynamic simulations in ADAMS revealed that pneumatic actuators provided smoother acceleration profiles, reduced peak forces during start-up, and minimized mechanical shocks. These findings align with those of Dettu et al. (2023), who reported similar benefits in precision agricultural machinery using pneumatic controls. Collectively, these results demonstrate that pneumatic systems not only improve operational efficiency but also enhance machine reliability, reduce environmental risks associated with oil leaks, and support the broader goal of sustainable agricultural mechanization. Conclusion This research confirms that integrating pneumatic actuators into combine harvesters can significantly enhance energy efficiency, operational speed, durability, and cost-effectiveness compared to conventional hydraulic and mechanical systems. Key findings include: A 23% reduction in energy consumption,A 28% decrease in unloading time,Improved threshing efficiency (91%) with reduced grain losses,Extended operational life up to 1200 hours,A 17% lower initial cost and a 35% reduced maintenance expenses. Given these advantages, pneumatic actuation represents a promising alternative for next-generation agricultural machinery aiming for sustainability, cost reduction, and improved productivity. Future studies should explore hybrid pneumatic-hydraulic systems and incorporate advanced control algorithms, such as fuzzy logic and machine learning, to further optimize system performance under diverse field conditions. Introduction Agricultural mechanization has become a crucial component in addressing the increasing global demand for food production while minimizing production costs and environmental impacts. Among agricultural machinery, combine harvesters play a vital role by integrating multiple harvesting processes such as cutting, threshing, separating, and unloading into a single operation. However, most conventional combine harvesters rely on hydraulic and mechanical actuation systems for controlling their primary functional units, including the header, threshing, and unloading mechanisms. Although these systems are well established, they are often characterized by relatively high energy consumption, complicated maintenance requirements, the risks of hydraulic oil leakage, and performance degradation under prolonged field use. In recent years, pneumatic actuation systems have gained attention in various agricultural and industrial applications due to their advantages, including simpler design, lower weight, faster response time, no risk of oil leakage, and reduced operational costs. Studies such as those by Johnson (2023) and Gryboś (2024) have reported significant energy-saving potentials for pneumatic systems compared to conventional drives in different industrial settings. However, the use of pneumatic systems in combine harvesters has been limited, and there is a lack of comprehensive research evaluating their performance under real field conditions, particularly regarding durability, energy efficiency, and operational reliability. This study was conducted to design, develop, and evaluate a pneumatic actuator (air cylinder) system as a substitute for traditional hydraulic and mechanical systems in combine harvesters. The main objectives were: To reduce energy consumption and operational time during harvesting. To improve the reliability, uniformity, and responsiveness of the drive system. To assess the durability and maintenance costs under real working conditions. To evaluate the economic feasibility of implementing pneumatic actuation in grain harvesters. Materials and Methods The study was carried out on a New Iran model straw combine harvester manufactured by Sabz Abad Hegmataneh Company in Hamedan Province, Iran. The field experiments were conducted on the Ehsan wheat variety. The research followed a multi-stage approach comprising conceptual design, 3D modeling, dynamic simulation, prototype development, and field testing. Initially, a complete 3D model of the pneumatic actuation system was developed in SolidWorks 2018. Dynamic simulations, including analyses of displacement, velocity, acceleration, and force analysis under varying loads, were performed using MSC ADAMS. The prototype was then integrated into the combine harvester to replace the conventional hydraulic and mechanical drives of three key units: The header unit for height adjustment,The threshing unit for concave clearance control,The unloading system for operating the auger pipe and grain discharge. The experimental design was a randomized complete block design (RCBD) with three replications and three treatments: pneumatic, hydraulic, and mechanical drive systems. Key performance indicators included: Energy consumption (kWh) measured using flow and pressure sensors,Unloading time (s) recorded using a stopwatch,Threshing efficiency (%) and uniformity of power transmission measured under field conditions,Durability (h) tested under dusty and humid environments,An economic evaluation of initial and annual maintenance costs. Statistical analyses were performed using IBM SPSS Statistics 26, employing independent t-tests and one-way ANOVA followed by Duncan’s multiple range tests at a 5% significance level. Results and Discussion The experimental results revealed that the pneumatic system significantly outperformed the conventional hydraulic and mechanical systems across all major performance parameters. Energy Consumption The pneumatic system consumed only 12.3 ± 0.8 kWh, representing a 23% reduction compared to the hydraulic (15.9 ± 1.1 kWh) and mechanical systems (16.4 ± 1.1 kWh). Similar energy-saving benefits were reported by Boyko and Weber (2024) in industrial pneumatic drives, indicating that air-actuated systems inherently require less energy due to the absence of continuous fluid pumping losses typical in hydraulic circuits. Unloading Time The unloading time of the grain tank decreased significantly from 54.3 ± 2.0 s (hydraulic) and 55.1 ± 2.1 s (mechanical) to 39.2 ± 1.5 s for the pneumatic system—a 28% reduction. Faster unloading allows for reduced combine downtime and improved field capacity, which is crucial for large-scale grain production systems. Threshing Efficiency and Power Transmission Uniformity Threshing efficiency reached 91.0 ± 2.2% with the pneumatic system, compared to 84.5 ± 2.5% for the hydraulic and 82.3 ± 0.3% for the mechanical systems. The smoother motion of pneumatic actuators minimized vibrations and shocks, reducing grain breakage and ensuring more uniform power delivery to the threshing drum. Durability and Reliability Durability testing under harsh field conditions (dust, moisture, and variable loads) showed that the pneumatic system maintained stable performance for 1200 operational hours, while the hydraulic and mechanical systems deteriorated after 930 h and 850 h, respectively. Reduced wear and the absence of hydraulic oil contamination were key contributing factors to the extended lifespan. Economic Evaluation The initial cost of the pneumatic system was 17% lower than that of the hydraulic system, while annual maintenance costs were reduced by 35%. The absence of hydraulic fluids, filters, and frequent servicing requirements resulted in significant long-term cost savings, making the pneumatic system economically attractive for farmers. Effect of Field Variables ANOVA results indicated that grain moisture content, threshing drum speed, and combine forward speed significantly influenced grain losses (p < 0.05). However, under optimized operational settings, the pneumatic system consistently exhibited lower grain loss and better performance than conventional systems. Simulation Insights Dynamic simulations in ADAMS revealed that pneumatic actuators provided smoother acceleration profiles, reduced peak forces during start-up, and minimized mechanical shocks. These findings align with those of Dettu et al. (2023), who reported similar benefits in precision agricultural machinery using pneumatic controls. Collectively, these results demonstrate that pneumatic systems not only improve operational efficiency but also enhance machine reliability, reduce environmental risks associated with oil leaks, and support the broader goal of sustainable agricultural mechanization. Conclusion This research confirms that integrating pneumatic actuators into combine harvesters can significantly enhance energy efficiency, operational speed, durability, and cost-effectiveness compared to conventional hydraulic and mechanical systems. Key findings include: A 23% reduction in energy consumption,A 28% decrease in unloading time,Improved threshing efficiency (91%) with reduced grain losses,Extended operational life up to 1200 hours,A 17% lower initial cost and a 35% reduced maintenance expenses. Given these advantages, pneumatic actuation represents a promising alternative for next-generation agricultural machinery aiming for sustainability, cost reduction, and improved productivity. Future studies should explore hybrid pneumatic-hydraulic systems and incorporate advanced control algorithms, such as fuzzy logic and machine learning, to further optimize system performance under diverse field conditions.

https://jam.tabrizu.ac.ir/article_20987_cc15007b67274f80f0e4b412d97e9c8d.pdf

University of Tabriz Journal of Agricultural Mechanization 2383-126X 10 4 2025 12 22 Analysis of Potential Regions in Iran for Greenhouse Tomato Cultivation with Emphasis on Heating and Cooling Requirements Using Geographic Information Systems (GIS) Analysis of Potential Regions in Iran for Greenhouse Tomato Cultivation with Emphasis on Heating and Cooling Requirements Using Geographic Information Systems (GIS) 15 25 20988 10.22034/jam.2025.68277.1337 FA Mohammad Khaledi Alamdari Ph.D. in Water Science and Engineering, Regional Water Company of East Aazarbaijan, Ministry of Energy, Tabriz, Iran. Ghasem Zarei Institute of Agricultural Sciences, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Vahid Kazem Alilou Institute of Agricultural Sciences, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran. Mosayeb Moqbeli Damane Department of Water Science and Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran. Journal Article 2025 07 22 Greenhouse cultivation has emerged as a strategic response to the growing challenges posed by population growth, urban expansion, resource limitations, and the increasing demand for off-season crops. In recent decades, rapid socio-economic transformations in developing countries such as Iran have intensified the need for sustainable agricultural methods that optimize resource consumption, particularly water and energy. Traditional open-field farming faces numerous limitations due to climate variability, land degradation, and dwindling groundwater resources, prompting a significant shift toward protected cultivation systems. Among these, greenhouse agriculture offers the advantages of higher crop yield, better control over growing conditions, and enhanced resource use efficiency. However, the feasibility and sustainability of greenhouse farming largely depend on local climate characteristics—specifically heating and cooling requirements, which directly influence energy consumption patterns. In order to maximize efficiency and minimize operational costs, it is essential to identify optimal locations for greenhouse development based on precise climatic criteria. Geographic Information Systems (GIS), when combined with meteorological data, provide powerful tools for spatial analysis and environmental suitability assessment. This study leverages GIS to evaluate the heating and cooling degree-day needs for greenhouse tomato cultivation across Iran, ultimately identifying regions with the lowest thermal energy requirements and offering practical insights for optimizing greenhouse location strategies. Introduction The increasing demand for food due to population growth, along with rapid economic and cultural development, has intensified the expansion of greenhouse cultivation worldwide. In Iran, several challenges such as water scarcity, declining groundwater levels, limited arable land, and the growing need to produce crops off-season have further emphasized the importance of greenhouse farming. Greenhouse cultivation offers numerous advantages, including enhanced crop yields, efficient water use, and reduced dependency on external environmental conditions. However, the optimal performance of greenhouses is highly dependent on local climatic factors, particularly thermal requirements. Heating and cooling demands vary significantly across different geographic regions, directly affecting energy consumption and economic feasibility. Therefore, identifying suitable locations for greenhouse construction based on thermal efficiency is essential. This study addresses the need to evaluate Iran’s diverse climate zones to determine the most appropriate areas for greenhouse tomato cultivation from the perspective of heating and cooling degree-day requirements using Geographic Information Systems (GIS). Materials and Methods To assess the suitability of different regions in Iran for greenhouse tomato production, a spatial analysis was conducted using GIS tools. Climatic data, including temperature records from synoptic weather stations across the country, were collected and processed to calculate heating and cooling degree-days (HDD and CDD) for each region. Degree-day indices were computed based on standard temperature thresholds associated with the growth requirements of greenhouse tomatoes. Thermal maps for both HDD and CDD were then generated to illustrate spatial variability across Iran. These maps served as the primary basis for identifying regions with minimum heating or cooling needs. The zoning was performed using interpolation methods, and final suitability maps were derived by integrating the thermal layers with geographic and climatic constraints relevant to greenhouse construction. Results and Discussion The results revealed significant spatial variation in thermal requirements for greenhouse tomato production across Iran. The heating demand ranged from 0 to 3500 degree-days annually, with the highest requirements observed in colder regions such as Firouzkouh in the north. Conversely, cooling needs varied from 0 to 2500 degree-days, with the highest values recorded in warmer areas like Shushtar in the southwest. Based on the thermal zoning maps, southern regions of Iran—particularly in the southern, southwestern, and southeastern parts—were identified as optimal locations for winter-season greenhouse tomato cultivation due to their minimal heating needs. These areas offer substantial energy-saving potential during colder months. In contrast, northern, northwestern, and northeastern parts of the country exhibited the lowest cooling requirements, making them suitable for summer-season greenhouse tomato production. The spatial distribution of thermal demand aligns well with energy-efficiency goals and can support a seasonal strategy to minimize input costs while maximizing productivity. Furthermore, the analysis highlights the significance of location-specific planning in greenhouse agriculture, emphasizing that thermal suitability should be a primary factor in site selection. Conclusion This study demonstrates the value of GIS-based spatial analysis for identifying thermally suitable regions for greenhouse tomato cultivation in Iran. By mapping heating and cooling degree-days, the research provides a comprehensive overview of climate-based suitability across the country. The findings suggest that optimizing the location of greenhouse structures according to seasonal thermal needs can significantly reduce energy consumption and improve economic efficiency. Southern Iran is best suited for off-season production during cooler months, while northern regions are favorable for warm-season cultivation. Integrating such climatic assessments into greenhouse development strategies can contribute to more sustainable and resilient agricultural systems in arid and semi-arid regions. Future studies may expand on this framework by incorporating additional factors such as solar radiation, humidity, and economic cost-benefit analyses to further refine location recommendations for greenhouse farming. Introduction The increasing demand for food due to population growth, along with rapid economic and cultural development, has intensified the expansion of greenhouse cultivation worldwide. In Iran, several challenges such as water scarcity, declining groundwater levels, limited arable land, and the growing need to produce crops off-season have further emphasized the importance of greenhouse farming. Greenhouse cultivation offers numerous advantages, including enhanced crop yields, efficient water use, and reduced dependency on external environmental conditions. However, the optimal performance of greenhouses is highly dependent on local climatic factors, particularly thermal requirements. Heating and cooling demands vary significantly across different geographic regions, directly affecting energy consumption and economic feasibility. Therefore, identifying suitable locations for greenhouse construction based on thermal efficiency is essential. This study addresses the need to evaluate Iran’s diverse climate zones to determine the most appropriate areas for greenhouse tomato cultivation from the perspective of heating and cooling degree-day requirements using Geographic Information Systems (GIS). Materials and Methods To assess the suitability of different regions in Iran for greenhouse tomato production, a spatial analysis was conducted using GIS tools. Climatic data, including temperature records from synoptic weather stations across the country, were collected and processed to calculate heating and cooling degree-days (HDD and CDD) for each region. Degree-day indices were computed based on standard temperature thresholds associated with the growth requirements of greenhouse tomatoes. Thermal maps for both HDD and CDD were then generated to illustrate spatial variability across Iran. These maps served as the primary basis for identifying regions with minimum heating or cooling needs. The zoning was performed using interpolation methods, and final suitability maps were derived by integrating the thermal layers with geographic and climatic constraints relevant to greenhouse construction. Results and Discussion The results revealed significant spatial variation in thermal requirements for greenhouse tomato production across Iran. The heating demand ranged from 0 to 3500 degree-days annually, with the highest requirements observed in colder regions such as Firouzkouh in the north. Conversely, cooling needs varied from 0 to 2500 degree-days, with the highest values recorded in warmer areas like Shushtar in the southwest. Based on the thermal zoning maps, southern regions of Iran—particularly in the southern, southwestern, and southeastern parts—were identified as optimal locations for winter-season greenhouse tomato cultivation due to their minimal heating needs. These areas offer substantial energy-saving potential during colder months. In contrast, northern, northwestern, and northeastern parts of the country exhibited the lowest cooling requirements, making them suitable for summer-season greenhouse tomato production. The spatial distribution of thermal demand aligns well with energy-efficiency goals and can support a seasonal strategy to minimize input costs while maximizing productivity. Furthermore, the analysis highlights the significance of location-specific planning in greenhouse agriculture, emphasizing that thermal suitability should be a primary factor in site selection. Conclusion This study demonstrates the value of GIS-based spatial analysis for identifying thermally suitable regions for greenhouse tomato cultivation in Iran. By mapping heating and cooling degree-days, the research provides a comprehensive overview of climate-based suitability across the country. The findings suggest that optimizing the location of greenhouse structures according to seasonal thermal needs can significantly reduce energy consumption and improve economic efficiency. Southern Iran is best suited for off-season production during cooler months, while northern regions are favorable for warm-season cultivation. Integrating such climatic assessments into greenhouse development strategies can contribute to more sustainable and resilient agricultural systems in arid and semi-arid regions. Future studies may expand on this framework by incorporating additional factors such as solar radiation, humidity, and economic cost-benefit analyses to further refine location recommendations for greenhouse farming. Greenhouse cultivation has emerged as a strategic response to the growing challenges posed by population growth, urban expansion, resource limitations, and the increasing demand for off-season crops. In recent decades, rapid socio-economic transformations in developing countries such as Iran have intensified the need for sustainable agricultural methods that optimize resource consumption, particularly water and energy. Traditional open-field farming faces numerous limitations due to climate variability, land degradation, and dwindling groundwater resources, prompting a significant shift toward protected cultivation systems. Among these, greenhouse agriculture offers the advantages of higher crop yield, better control over growing conditions, and enhanced resource use efficiency. However, the feasibility and sustainability of greenhouse farming largely depend on local climate characteristics—specifically heating and cooling requirements, which directly influence energy consumption patterns. In order to maximize efficiency and minimize operational costs, it is essential to identify optimal locations for greenhouse development based on precise climatic criteria. Geographic Information Systems (GIS), when combined with meteorological data, provide powerful tools for spatial analysis and environmental suitability assessment. This study leverages GIS to evaluate the heating and cooling degree-day needs for greenhouse tomato cultivation across Iran, ultimately identifying regions with the lowest thermal energy requirements and offering practical insights for optimizing greenhouse location strategies. Introduction The increasing demand for food due to population growth, along with rapid economic and cultural development, has intensified the expansion of greenhouse cultivation worldwide. In Iran, several challenges such as water scarcity, declining groundwater levels, limited arable land, and the growing need to produce crops off-season have further emphasized the importance of greenhouse farming. Greenhouse cultivation offers numerous advantages, including enhanced crop yields, efficient water use, and reduced dependency on external environmental conditions. However, the optimal performance of greenhouses is highly dependent on local climatic factors, particularly thermal requirements. Heating and cooling demands vary significantly across different geographic regions, directly affecting energy consumption and economic feasibility. Therefore, identifying suitable locations for greenhouse construction based on thermal efficiency is essential. This study addresses the need to evaluate Iran’s diverse climate zones to determine the most appropriate areas for greenhouse tomato cultivation from the perspective of heating and cooling degree-day requirements using Geographic Information Systems (GIS). Materials and Methods To assess the suitability of different regions in Iran for greenhouse tomato production, a spatial analysis was conducted using GIS tools. Climatic data, including temperature records from synoptic weather stations across the country, were collected and processed to calculate heating and cooling degree-days (HDD and CDD) for each region. Degree-day indices were computed based on standard temperature thresholds associated with the growth requirements of greenhouse tomatoes. Thermal maps for both HDD and CDD were then generated to illustrate spatial variability across Iran. These maps served as the primary basis for identifying regions with minimum heating or cooling needs. The zoning was performed using interpolation methods, and final suitability maps were derived by integrating the thermal layers with geographic and climatic constraints relevant to greenhouse construction. Results and Discussion The results revealed significant spatial variation in thermal requirements for greenhouse tomato production across Iran. The heating demand ranged from 0 to 3500 degree-days annually, with the highest requirements observed in colder regions such as Firouzkouh in the north. Conversely, cooling needs varied from 0 to 2500 degree-days, with the highest values recorded in warmer areas like Shushtar in the southwest. Based on the thermal zoning maps, southern regions of Iran—particularly in the southern, southwestern, and southeastern parts—were identified as optimal locations for winter-season greenhouse tomato cultivation due to their minimal heating needs. These areas offer substantial energy-saving potential during colder months. In contrast, northern, northwestern, and northeastern parts of the country exhibited the lowest cooling requirements, making them suitable for summer-season greenhouse tomato production. The spatial distribution of thermal demand aligns well with energy-efficiency goals and can support a seasonal strategy to minimize input costs while maximizing productivity. Furthermore, the analysis highlights the significance of location-specific planning in greenhouse agriculture, emphasizing that thermal suitability should be a primary factor in site selection. Conclusion This study demonstrates the value of GIS-based spatial analysis for identifying thermally suitable regions for greenhouse tomato cultivation in Iran. By mapping heating and cooling degree-days, the research provides a comprehensive overview of climate-based suitability across the country. The findings suggest that optimizing the location of greenhouse structures according to seasonal thermal needs can significantly reduce energy consumption and improve economic efficiency. Southern Iran is best suited for off-season production during cooler months, while northern regions are favorable for warm-season cultivation. Integrating such climatic assessments into greenhouse development strategies can contribute to more sustainable and resilient agricultural systems in arid and semi-arid regions. Future studies may expand on this framework by incorporating additional factors such as solar radiation, humidity, and economic cost-benefit analyses to further refine location recommendations for greenhouse farming. Introduction The increasing demand for food due to population growth, along with rapid economic and cultural development, has intensified the expansion of greenhouse cultivation worldwide. In Iran, several challenges such as water scarcity, declining groundwater levels, limited arable land, and the growing need to produce crops off-season have further emphasized the importance of greenhouse farming. Greenhouse cultivation offers numerous advantages, including enhanced crop yields, efficient water use, and reduced dependency on external environmental conditions. However, the optimal performance of greenhouses is highly dependent on local climatic factors, particularly thermal requirements. Heating and cooling demands vary significantly across different geographic regions, directly affecting energy consumption and economic feasibility. Therefore, identifying suitable locations for greenhouse construction based on thermal efficiency is essential. This study addresses the need to evaluate Iran’s diverse climate zones to determine the most appropriate areas for greenhouse tomato cultivation from the perspective of heating and cooling degree-day requirements using Geographic Information Systems (GIS). Materials and Methods To assess the suitability of different regions in Iran for greenhouse tomato production, a spatial analysis was conducted using GIS tools. Climatic data, including temperature records from synoptic weather stations across the country, were collected and processed to calculate heating and cooling degree-days (HDD and CDD) for each region. Degree-day indices were computed based on standard temperature thresholds associated with the growth requirements of greenhouse tomatoes. Thermal maps for both HDD and CDD were then generated to illustrate spatial variability across Iran. These maps served as the primary basis for identifying regions with minimum heating or cooling needs. The zoning was performed using interpolation methods, and final suitability maps were derived by integrating the thermal layers with geographic and climatic constraints relevant to greenhouse construction. Results and Discussion The results revealed significant spatial variation in thermal requirements for greenhouse tomato production across Iran. The heating demand ranged from 0 to 3500 degree-days annually, with the highest requirements observed in colder regions such as Firouzkouh in the north. Conversely, cooling needs varied from 0 to 2500 degree-days, with the highest values recorded in warmer areas like Shushtar in the southwest. Based on the thermal zoning maps, southern regions of Iran—particularly in the southern, southwestern, and southeastern parts—were identified as optimal locations for winter-season greenhouse tomato cultivation due to their minimal heating needs. These areas offer substantial energy-saving potential during colder months. In contrast, northern, northwestern, and northeastern parts of the country exhibited the lowest cooling requirements, making them suitable for summer-season greenhouse tomato production. The spatial distribution of thermal demand aligns well with energy-efficiency goals and can support a seasonal strategy to minimize input costs while maximizing productivity. Furthermore, the analysis highlights the significance of location-specific planning in greenhouse agriculture, emphasizing that thermal suitability should be a primary factor in site selection. Conclusion This study demonstrates the value of GIS-based spatial analysis for identifying thermally suitable regions for greenhouse tomato cultivation in Iran. By mapping heating and cooling degree-days, the research provides a comprehensive overview of climate-based suitability across the country. The findings suggest that optimizing the location of greenhouse structures according to seasonal thermal needs can significantly reduce energy consumption and improve economic efficiency. Southern Iran is best suited for off-season production during cooler months, while northern regions are favorable for warm-season cultivation. Integrating such climatic assessments into greenhouse development strategies can contribute to more sustainable and resilient agricultural systems in arid and semi-arid regions. Future studies may expand on this framework by incorporating additional factors such as solar radiation, humidity, and economic cost-benefit analyses to further refine location recommendations for greenhouse farming.

https://jam.tabrizu.ac.ir/article_20988_c39447f019e3c898ad2b03e8cea12c80.pdf

University of Tabriz Journal of Agricultural Mechanization 2383-126X 10 4 2025 12 22 A Novel AI-Based Machine Vision Approach for Detecting Thrips Damage Symptoms on Cucumber Leaves A Novel AI-Based Machine Vision Approach for Detecting Thrips Damage Symptoms on Cucumber Leaves 27 39 21296 10.22034/jam.2026.68067.1332 FA Hossein Akhtari Department of Biosystem Engineering, Faculty of Agriculture, University of Tabriz, Tabriz. Iran. Hossein Navid Department of Biosystem Engineering, Faculty of Agriculture, University of Tabriz, Tabriz. Iran. Roghaiyeh Karimzadeh Department of Plant Protection, Faculty of Agriculture, University of Tabriz, Tabriz, Iran. Nayer Etminanfar Department of Biosystem Engineering, Faculty of Agriculture, University of Tabriz, Tabriz. Iran. Journal Article 2025 07 12 Introduction The integration of artificial intelligence and machine vision into agriculture has opened new horizons for the early detection of pests and plant diseases. These technologies are particularly valuable in greenhouse environments, where rapid intervention is essential to minimize crop losses. Traditional approaches have predominantly relied on static image classification, which lacks the ability to capture temporal dynamics of symptoms such as progressive leaf damage. In this context, this study explores the use of video-based deep learning for the detection of thrips induced damage on cucumber leaves. By comparing the performance of two modern spatiotemporal models MovieNet and SlowFast, the research aims to identify a reliable solution for real-time, accurate detection of pest damage in greenhouse conditions. Materials and Methods This study aimed to investigate the effectiveness of deep learning models, particularly convolutional neural networks, in detecting thrips-infested cucumber leaves using video-based input rather than still images. To this end, a dataset of 606 images was collected from the research greenhouse of the University of Tabriz, including 274 images of healthy leaves and 332 images of thrips-infested leaves.Given the widespread presence of this pest in the greenhouse, images were captured under natural conditions from various angles and distances to enhance data diversity and improve the model’s generalization to real-world scenarios. Care was also taken to ensure similar conditions when capturing images of healthy leaves to maintain class balance. To simulate temporal dynamics and enable video-based learning, the collected images were converted into short video clips. Specifically, every four randomly selected images were combined sequentially to form one video. The frame rate was deliberately set to a low value of 3 frames per second to facilitate meaningful temporal feature extraction. After this preprocessing step and applying several offline data augmentation techniques, the final dataset comprised 909 videos, including 411 videos of healthy leaves and 498 videos of thrips-infected leaves. For the learning task, two deep spatiotemporal architectures were employed MovieNet and SlowFast. Both models are known for their ability to capture motion and spatial patterns effectively. Prior to training, the video data were split into training (70%), validation (15%), and test (15%) sets using stratified sampling to preserve class distribution across subsets. All videos were resized and normalized according to the input requirements of the respective architectures. The models were trained with learning hyperparameters optimally tuned to ensure effective convergence and to minimize overfitting. Performance evaluation was conducted using common classification metrics, including accuracy, precision, recall, and F1-score, computed on the test set. Additionally, confusion matrices and training-validation loss curves were analyzed to further assess model behavior during training and generalization capability. Results and Discussion Training-validation loss curves highlighted key differences between the two models. In the case of MovieNet, the training and validation loss both decreased rapidly at the beginning, indicating effective learning. However, during later epochs, the validation loss diverged from the training loss, suggesting overfitting. The model achieved 100% test accuracy, but this was considered unreliable due to the relatively small test set and the model’s tendency to memorize rather than generalize. Conversely, SlowFast demonstrated fluctuating loss values during the initial training phases, possibly due to its more complex architecture and optimization strategy. Despite the instability early on, both training and validation losses eventually converged, indicating improved generalization. This model achieved a final test accuracy of 99.27%, with a test loss of 0.0425, reflecting strong performance. Detailed classwise evaluation revealed that the healthy leaf class achieved 98.41% precision and 100% recall, indicating that no healthy samples were misclassified. The thrips-damaged class recorded 100% precision and 98.67% recall, suggesting high detection accuracy with minimal false negatives. The overall F1-score, precision, and recall all exceeded 99%, confirming balanced and accurate performance across both classes. The confusion matrix further validated these results. All 62 healthy samples were correctly classified, with zero misclassifications. Among the 75 thrips-damaged samples, 74 were correctly identified, with only one instance misclassified as healthy. This minimal error highlights the robustness of the SlowFast model in binary classification of pest damage. Conclusion This research demonstrates the efficacy of video-based deep learning methods for detecting thrips damage on cucumber leaves in greenhouse environments. Unlike conventional static image approaches, video enables the capture of dynamic changes and subtle visual cues over time, enhancing model accuracy and reliability. Between the two models tested, SlowFast outperformed MovieNet, providing superior generalization and higher classification accuracy without overfitting. Its architectural design, particularly the dual-pathway temporal processing and ResNet-50 backbone, enabled it to achieve a final test accuracy of 99.27% and excellent precision-recall balance across both classes. This video-based approach demonstrated several key advantages over traditional image-based methods, including enhanced accuracy through the capture of temporal symptom progression, reduced misclassification caused by static noise, and improved pattern recognition in dynamic real-world scenarios. These strengths highlight the potential of video-based deep learning techniques for integration into intelligent monitoring systems in modern greenhouses, offering farmers the ability to detect and respond to pest infestations more promptly and effectively Future work should explore multi-class detection of various pests and diseases, as well as the incorporation of attention mechanisms or transformer-based video models to further improve accuracy. Additionally, developing mobile or cloud-based platforms for model deployment could make this technology more accessible for real-world agricultural applications. Acknowledgement The authors would like to thank the students working in the greenhouse for their cooperation and for allowing data collection during their research activities. Introduction The integration of artificial intelligence and machine vision into agriculture has opened new horizons for the early detection of pests and plant diseases. These technologies are particularly valuable in greenhouse environments, where rapid intervention is essential to minimize crop losses. Traditional approaches have predominantly relied on static image classification, which lacks the ability to capture temporal dynamics of symptoms such as progressive leaf damage. In this context, this study explores the use of video-based deep learning for the detection of thrips induced damage on cucumber leaves. By comparing the performance of two modern spatiotemporal models MovieNet and SlowFast, the research aims to identify a reliable solution for real-time, accurate detection of pest damage in greenhouse conditions. Materials and Methods This study aimed to investigate the effectiveness of deep learning models, particularly convolutional neural networks, in detecting thrips-infested cucumber leaves using video-based input rather than still images. To this end, a dataset of 606 images was collected from the research greenhouse of the University of Tabriz, including 274 images of healthy leaves and 332 images of thrips-infested leaves.Given the widespread presence of this pest in the greenhouse, images were captured under natural conditions from various angles and distances to enhance data diversity and improve the model’s generalization to real-world scenarios. Care was also taken to ensure similar conditions when capturing images of healthy leaves to maintain class balance. To simulate temporal dynamics and enable video-based learning, the collected images were converted into short video clips. Specifically, every four randomly selected images were combined sequentially to form one video. The frame rate was deliberately set to a low value of 3 frames per second to facilitate meaningful temporal feature extraction. After this preprocessing step and applying several offline data augmentation techniques, the final dataset comprised 909 videos, including 411 videos of healthy leaves and 498 videos of thrips-infected leaves. For the learning task, two deep spatiotemporal architectures were employed MovieNet and SlowFast. Both models are known for their ability to capture motion and spatial patterns effectively. Prior to training, the video data were split into training (70%), validation (15%), and test (15%) sets using stratified sampling to preserve class distribution across subsets. All videos were resized and normalized according to the input requirements of the respective architectures. The models were trained with learning hyperparameters optimally tuned to ensure effective convergence and to minimize overfitting. Performance evaluation was conducted using common classification metrics, including accuracy, precision, recall, and F1-score, computed on the test set. Additionally, confusion matrices and training-validation loss curves were analyzed to further assess model behavior during training and generalization capability. Results and Discussion Training-validation loss curves highlighted key differences between the two models. In the case of MovieNet, the training and validation loss both decreased rapidly at the beginning, indicating effective learning. However, during later epochs, the validation loss diverged from the training loss, suggesting overfitting. The model achieved 100% test accuracy, but this was considered unreliable due to the relatively small test set and the model’s tendency to memorize rather than generalize. Conversely, SlowFast demonstrated fluctuating loss values during the initial training phases, possibly due to its more complex architecture and optimization strategy. Despite the instability early on, both training and validation losses eventually converged, indicating improved generalization. This model achieved a final test accuracy of 99.27%, with a test loss of 0.0425, reflecting strong performance. Detailed classwise evaluation revealed that the healthy leaf class achieved 98.41% precision and 100% recall, indicating that no healthy samples were misclassified. The thrips-damaged class recorded 100% precision and 98.67% recall, suggesting high detection accuracy with minimal false negatives. The overall F1-score, precision, and recall all exceeded 99%, confirming balanced and accurate performance across both classes. The confusion matrix further validated these results. All 62 healthy samples were correctly classified, with zero misclassifications. Among the 75 thrips-damaged samples, 74 were correctly identified, with only one instance misclassified as healthy. This minimal error highlights the robustness of the SlowFast model in binary classification of pest damage. Conclusion This research demonstrates the efficacy of video-based deep learning methods for detecting thrips damage on cucumber leaves in greenhouse environments. Unlike conventional static image approaches, video enables the capture of dynamic changes and subtle visual cues over time, enhancing model accuracy and reliability. Between the two models tested, SlowFast outperformed MovieNet, providing superior generalization and higher classification accuracy without overfitting. Its architectural design, particularly the dual-pathway temporal processing and ResNet-50 backbone, enabled it to achieve a final test accuracy of 99.27% and excellent precision-recall balance across both classes. This video-based approach demonstrated several key advantages over traditional image-based methods, including enhanced accuracy through the capture of temporal symptom progression, reduced misclassification caused by static noise, and improved pattern recognition in dynamic real-world scenarios. These strengths highlight the potential of video-based deep learning techniques for integration into intelligent monitoring systems in modern greenhouses, offering farmers the ability to detect and respond to pest infestations more promptly and effectively Future work should explore multi-class detection of various pests and diseases, as well as the incorporation of attention mechanisms or transformer-based video models to further improve accuracy. Additionally, developing mobile or cloud-based platforms for model deployment could make this technology more accessible for real-world agricultural applications. Acknowledgement The authors would like to thank the students working in the greenhouse for their cooperation and for allowing data collection during their research activities.

https://jam.tabrizu.ac.ir/article_21296_c3db9df1eb21182c52e24d4317e66060.pdf

University of Tabriz Journal of Agricultural Mechanization 2383-126X 10 4 2025 12 22 Design, Fabrication, and Mechanical Performance Evaluation of a Saffron Flower Harvesting Blade Design, Fabrication, and Mechanical Performance Evaluation of a Saffron Flower Harvesting Blade 41 55 21356 10.22034/jam.2026.70141.1345 FA Faraneh Khodamoradi Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran. Mojtaba Jaberimoeaz Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran. Journal Article 2025 11 10 Saffron (Crocus sativus L.) is one of the world’s most valuable agricultural products, with Iran accounting for over 90% of global production. Harvesting is predominantly manual, resulting in high labor demands, significant ergonomic strain on workers, and considerable floral damage. The narrow blooming window—typically limited to 7–10 days—and the extreme mechanical fragility of the stigma-pistil complex impose stringent requirements on harvesting precision and speed. These constraints underscore the urgent need for mechanized solutions that can maintain product quality while improving operational efficiency and worker welfare. Introduction Saffron (Crocus sativus L.), renowned as “red gold,” is among the world’s most valuable agricultural commodities, valued for its unique coloring, aromatic, and pharmacological properties. Iran, contributing over 90% of global production, remains heavily dependent on manual harvesting—a labor-intensive practice that imposes severe ergonomic burdens on workers, particularly repetitive stress injuries to the lumbar spine and knee joints due to prolonged bending and squatting. The harvesting window is critically narrow, typically confined to 7–10 days of synchronous blooming, during which flowers must be collected at dawn to preserve stigma quality. This time sensitivity, combined with the extreme fragility of the stigma-pistil complex, renders the process highly susceptible to quality degradation when performed manually. Despite the widespread mechanization of major field crops, saffron harvesting has resisted automation due to three primary challenges: The delicate morphological structure of the flower, comprising three stigmas, three stamens, and six petals supported by a slender pedicel (~2 mm diameter); (ii) the high risk of mechanical damage during detachment, which directly compromises the economic value of the stigmas; and (iii) field heterogeneity, including uneven terrain, variable plant density, and inconsistent flower height. Existing prototypes—such as rotary rollers, pneumatic suction systems, and multi-tine pickers—have generally failed under real-world conditions, either causing excessive floral injury or requiring impractical power inputs. Recent research into aerodynamic separation and electrostatic harvesting has shown promise, yet these approaches often neglect the biomechanical response of saffron tissues under dynamic loading. To bridge this gap, this study proposes a biomimetic harvesting mechanism inspired by the precision of manual picking. A dedicated detachment blade was developed based on empirical characterization of saffron’s physical and mechanical properties. The system was evaluated under actual field conditions with respect to harvesting efficiency, flower integrity, adaptability to terrain irregularities, and mechanical reliability. The overarching objective is to establish a scientifically grounded foundation for semi-mechanized saffron harvesting that harmonizes plant-specific fragility with engineering robustness. Materials and Methods Saffron flowers consist of a central pistil bearing three vivid red stigmas—the sole economically valuable component—surrounded by three yellow stamens and six violet petals. To inform the design of a compatible harvesting tool, key physical and biomechanical properties were quantified under field-moist conditions. Moisture content was determined using the oven-drying method yielding values of 89.5% (w.b.) in stems and 78.2% in petals. Stem diameter was measured at the detachment zone (pedicel base) using a digital caliper (±0.01 mm), resulting in an average of 2.1 ± 0.3 mm. The force required for floral detachment was assessed via quasi-static uniaxial tensile tests conducted with a universal testing machine (Instron 3345, 10 N load cell, crosshead speed: 5 mm·min⁻¹). Flowers were gripped at the stigma base, and force was applied vertically until separation occurred. The detachment force was defined as the peak load preceding complete rupture. Based on these data, a harvesting blade was fabricated from austenitic stainless steel (AISI 304, yield strength = 220.6 MPa) using precision laser cutting (kerf width: 0.2 mm). The blade geometry featured a concave cutting edge designed to cradle the stem and guide it into the shear zone, minimizing lateral displacement and ensuring clean severance below the stigma attachment point. A polylactic acid (PLA) spacer, 3D-printed with 0.1 mm layer resolution, maintained uniform inter-blade spacing (4.5 mm) and alignment along the rotating shaft. Operational parameters were optimized through kinematic analysis. A rotational speed of 245 rpm was selected to achieve a blade tip linear velocity of 1.8 m·s⁻¹—sufficient to induce rapid detachment while avoiding inertial damage. Structural integrity was evaluated via finite element analysis (FEA) in SolidWorks Simulation 2018. A high-density mesh (element size: 0.4 mm at stress concentration zones) was applied to the blade tip, with boundary conditions replicating the measured 0.46 N detachment force. Theoretical stress was calculated using Euler–Bernoulli beam theory for cantilevered loading. Field trials were conducted in a commercial saffron field (Khorasan, Iran) during peak bloom. Performance metrics included effective field capacity, percentage of damaged flowers (classified by stigma bruising, petal tearing, or stem bending), and adaptability to micro-terrain variations. A protective elastomeric layer (Shore A 70) was tested to assess its impact-dampening efficacy. Results and Discussion Quasi-static testing yielded a mean detachment force of 0.46 ± 0.08 N, consistent with the low tensile strength of saffron pedicel tissues under high moisture conditions. This low force threshold dictated the necessity of a controlled, non-impact harvesting mechanism. FEA of the blade under operational loading revealed a maximum von Mises stress of 104.1 MPa at the tip root—the critical failure location. Theoretical beam-bending analysis predicted a stress of 188.3 MPa, with the discrepancy attributed to idealized assumptions in the analytical model (e.g., perfect clamping, homogeneous material). Critically, both values remained well below the yield strength of AISI 304 (220.6 MPa), confirming a safety factor of ≥2.1 and eliminating the risk of plastic deformation during field operation (HassanBeigi et al., 2010). Field evaluations demonstrated an effective field capacity of 0.42 t·h⁻¹, markedly higher than the manual benchmark of 0.09 . The inclusion of an elastomeric protective layer reduced the percentage of damaged flowers from 23.7 ± 2.8% (bare metal configuration) to 8.2 ± 1.3%—a 65.4% reduction (p < 0.01, two-tailed t-test). Damage in the protected system was limited primarily to minor petal detachment, whereas the unprotected variant exhibited severe stigma bruising and style bending, directly impairing saffron quality. The sequential blade arrangement ensured uniform coverage across the row width (15 cm), eliminating flower retention in inter-blade zones—a common flaw in prior designs. These results confirm that successful saffron mechanization hinges not on brute-force automation, but on biomechanical fidelity: the precise matching of tool dynamics to plant structural response. The concave blade edge, optimized tip velocity, and elastomeric interface collectively replicate the dexterity of manual picking while offering scalable throughput. Conclusion This study demonstrates that a scientifically informed, plant-centric approach to tool design can overcome the longstanding barriers to saffron harvesting mechanization. By integrating empirical biomechanical data—particularly the low detachment force (0.46 N) and high tissue moisture—into the geometric and material configuration of a specialized harvesting blade, we achieved a system that simultaneously ensures flower integrity, mechanical reliability, and field-level efficiency.The blade’s stress response (104.1 MPa) remains safely within elastic limits, validating the structural design under real operational loads. The 65.4% reduction in floral damage through elastomeric protection underscores the critical role of contact surface engineering in preserving stigma quality. Furthermore, the 4.7-fold increase in field capacity over manual methods highlights the system’s potential to alleviate labor shortages and reduce occupational health risks. This work establishes a transferable framework for the mechanization of high-value, mechanically sensitive crops: one that prioritizes biological compatibility over mechanical dominance. Future efforts will focus on scaling the prototype to multi-row configurations and integrating real-time vision systems for selective harvesting.Introduction Saffron (Crocus sativus L.), renowned as “red gold,” is among the world’s most valuable agricultural commodities, valued for its unique coloring, aromatic, and pharmacological properties. Iran, contributing over 90% of global production, remains heavily dependent on manual harvesting—a labor-intensive practice that imposes severe ergonomic burdens on workers, particularly repetitive stress injuries to the lumbar spine and knee joints due to prolonged bending and squatting. The harvesting window is critically narrow, typically confined to 7–10 days of synchronous blooming, during which flowers must be collected at dawn to preserve stigma quality. This time sensitivity, combined with the extreme fragility of the stigma-pistil complex, renders the process highly susceptible to quality degradation when performed manually. Despite the widespread mechanization of major field crops, saffron harvesting has resisted automation due to three primary challenges: the delicate morphological structure of the flower, comprising three stigmas, three stamens, and six petals supported by a slender pedicel (~2 mm diameter); (ii) the high risk of mechanical damage during detachment, which directly compromises the economic value of the stigmas; and (iii) field heterogeneity, including uneven terrain, variable plant density, and inconsistent flower height. Existing prototypes—such as rotary rollers, pneumatic suction systems, and multi-tine pickers—have generally failed under real-world conditions, either causing excessive floral injury or requiring impractical power inputs. Recent research into aerodynamic separation and electrostatic harvesting has shown promise, yet these approaches often neglect the biomechanical response of saffron tissues under dynamic loading. To bridge this gap, this study proposes a biomimetic harvesting mechanism inspired by the precision of manual picking. A dedicated detachment blade was developed based on empirical characterization of saffron’s physical and mechanical properties. The system was evaluated under actual field conditions with respect to harvesting efficiency, flower integrity, adaptability to terrain irregularities, and mechanical reliability. The overarching objective is to establish a scientifically grounded foundation for semi-mechanized saffron harvesting that harmonizes plant-specific fragility with engineering robustness. Materials and Methods Saffron flowers consist of a central pistil bearing three vivid red stigmas—the sole economically valuable component—surrounded by three yellow stamens and six violet petals. To inform the design of a compatible harvesting tool, key physical and biomechanical properties were quantified under field-moist conditions. Moisture content was determined using the oven-drying method yielding values of 89.5% (w.b.) in stems and 78.2% in petals. Stem diameter was measured at the detachment zone (pedicel base) using a digital caliper (±0.01 mm), resulting in an average of 2.1 ± 0.3 mm. The force required for floral detachment was assessed via quasi-static uniaxial tensile tests conducted with a universal testing machine (Instron 3345, 10 N load cell, crosshead speed: 5 mm·min⁻¹). Flowers were gripped at the stigma base, and force was applied vertically until separation occurred. The detachment force was defined as the peak load preceding complete rupture. Based on these data, a harvesting blade was fabricated from austenitic stainless steel (AISI 304, yield strength = 220.6 MPa) using precision laser cutting (kerf width: 0.2 mm). The blade geometry featured a concave cutting edge designed to cradle the stem and guide it into the shear zone, minimizing lateral displacement and ensuring clean severance below the stigma attachment point. A polylactic acid (PLA) spacer, 3D-printed with 0.1 mm layer resolution, maintained uniform inter-blade spacing (4.5 mm) and alignment along the rotating shaft. Operational parameters were optimized through kinematic analysis. A rotational speed of 245 rpm was selected to achieve a blade tip linear velocity of 1.8 m·s⁻¹—sufficient to induce rapid detachment while avoiding inertial damage. Structural integrity was evaluated via finite element analysis (FEA) in SolidWorks Simulation 2018. A high-density mesh (element size: 0.4 mm at stress concentration zones) was applied to the blade tip, with boundary conditions replicating the measured 0.46 N detachment force. Theoretical stress was calculated using Euler–Bernoulli beam theory for cantilevered loading. Field trials were conducted in a commercial saffron field (Khorasan, Iran) during peak bloom. Performance metrics included effective field capacity, percentage of damaged flowers (classified by stigma bruising, petal tearing, or stem bending), and adaptability to micro-terrain variations. A protective elastomeric layer (Shore A 70) was tested to assess its impact-dampening efficacy. Results and Discussion Quasi-static testing yielded a mean detachment force of 0.46 ± 0.08 N, consistent with the low tensile strength of saffron pedicel tissues under high moisture conditions. This low force threshold dictated the necessity of a controlled, non-impact harvesting mechanism. FEA of the blade under operational loading revealed a maximum von Mises stress of 104.1 MPa at the tip root—the critical failure location. Theoretical beam-bending analysis predicted a stress of 188.3 MPa, with the discrepancy attributed to idealized assumptions in the analytical model (e.g., perfect clamping, homogeneous material). Critically, both values remained well below the yield strength of AISI 304 (220.6 MPa), confirming a safety factor of ≥2.1 and eliminating the risk of plastic deformation during field operation (HassanBeigi et al., 2010). Field evaluations demonstrated an effective field capacity of 0.42 t·h⁻¹, markedly higher than the manual benchmark of 0.09 . The inclusion of an elastomeric protective layer reduced the percentage of damaged flowers from 23.7 ± 2.8% (bare metal configuration) to 8.2 ± 1.3%—a 65.4% reduction (p < 0.01, two-tailed t-test). Damage in the protected system was limited primarily to minor petal detachment, whereas the unprotected variant exhibited severe stigma bruising and style bending, directly impairing saffron quality. The sequential blade arrangement ensured uniform coverage across the row width (15 cm), eliminating flower retention in inter-blade zones—a common flaw in prior designs. These results confirm that successful saffron mechanization hinges not on brute-force automation, but on biomechanical fidelity: the precise matching of tool dynamics to plant structural response. The concave blade edge, optimized tip velocity, and elastomeric interface collectively replicate the dexterity of manual picking while offering scalable throughput. Conclusion This study demonstrates that a scientifically informed, plant-centric approach to tool design can overcome the longstanding barriers to saffron harvesting mechanization. By integrating empirical biomechanical data—particularly the low detachment force (0.46 N) and high tissue moisture—into the geometric and material configuration of a specialized harvesting blade, we achieved a system that simultaneously ensures flower integrity, mechanical reliability, and field-level efficiency. The blade’s stress response (104.1 MPa) remains safely within elastic limits, validating the structural design under real operational loads. The 65.4% reduction in floral damage through elastomeric protection underscores the critical role of contact surface engineering in preserving stigma quality. Furthermore, the 4.7-fold increase in field capacity over manual methods highlights the system’s potential to alleviate labor shortages and reduce occupational health risks. This work establishes a transferable framework for the mechanization of high-value, mechanically sensitive crops: one that prioritizes biological compatibility over mechanical dominance. Future efforts will focus on scaling the prototype to multi-row configurations and integrating real-time vision systems for selective harvesting. Saffron (Crocus sativus L.) is one of the world’s most valuable agricultural products, with Iran accounting for over 90% of global production. Harvesting is predominantly manual, resulting in high labor demands, significant ergonomic strain on workers, and considerable floral damage. The narrow blooming window—typically limited to 7–10 days—and the extreme mechanical fragility of the stigma-pistil complex impose stringent requirements on harvesting precision and speed. These constraints underscore the urgent need for mechanized solutions that can maintain product quality while improving operational efficiency and worker welfare. Introduction Saffron (Crocus sativus L.), renowned as “red gold,” is among the world’s most valuable agricultural commodities, valued for its unique coloring, aromatic, and pharmacological properties. Iran, contributing over 90% of global production, remains heavily dependent on manual harvesting—a labor-intensive practice that imposes severe ergonomic burdens on workers, particularly repetitive stress injuries to the lumbar spine and knee joints due to prolonged bending and squatting. The harvesting window is critically narrow, typically confined to 7–10 days of synchronous blooming, during which flowers must be collected at dawn to preserve stigma quality. This time sensitivity, combined with the extreme fragility of the stigma-pistil complex, renders the process highly susceptible to quality degradation when performed manually. Despite the widespread mechanization of major field crops, saffron harvesting has resisted automation due to three primary challenges: The delicate morphological structure of the flower, comprising three stigmas, three stamens, and six petals supported by a slender pedicel (~2 mm diameter); (ii) the high risk of mechanical damage during detachment, which directly compromises the economic value of the stigmas; and (iii) field heterogeneity, including uneven terrain, variable plant density, and inconsistent flower height. Existing prototypes—such as rotary rollers, pneumatic suction systems, and multi-tine pickers—have generally failed under real-world conditions, either causing excessive floral injury or requiring impractical power inputs. Recent research into aerodynamic separation and electrostatic harvesting has shown promise, yet these approaches often neglect the biomechanical response of saffron tissues under dynamic loading. To bridge this gap, this study proposes a biomimetic harvesting mechanism inspired by the precision of manual picking. A dedicated detachment blade was developed based on empirical characterization of saffron’s physical and mechanical properties. The system was evaluated under actual field conditions with respect to harvesting efficiency, flower integrity, adaptability to terrain irregularities, and mechanical reliability. The overarching objective is to establish a scientifically grounded foundation for semi-mechanized saffron harvesting that harmonizes plant-specific fragility with engineering robustness. Materials and Methods Saffron flowers consist of a central pistil bearing three vivid red stigmas—the sole economically valuable component—surrounded by three yellow stamens and six violet petals. To inform the design of a compatible harvesting tool, key physical and biomechanical properties were quantified under field-moist conditions. Moisture content was determined using the oven-drying method yielding values of 89.5% (w.b.) in stems and 78.2% in petals. Stem diameter was measured at the detachment zone (pedicel base) using a digital caliper (±0.01 mm), resulting in an average of 2.1 ± 0.3 mm. The force required for floral detachment was assessed via quasi-static uniaxial tensile tests conducted with a universal testing machine (Instron 3345, 10 N load cell, crosshead speed: 5 mm·min⁻¹). Flowers were gripped at the stigma base, and force was applied vertically until separation occurred. The detachment force was defined as the peak load preceding complete rupture. Based on these data, a harvesting blade was fabricated from austenitic stainless steel (AISI 304, yield strength = 220.6 MPa) using precision laser cutting (kerf width: 0.2 mm). The blade geometry featured a concave cutting edge designed to cradle the stem and guide it into the shear zone, minimizing lateral displacement and ensuring clean severance below the stigma attachment point. A polylactic acid (PLA) spacer, 3D-printed with 0.1 mm layer resolution, maintained uniform inter-blade spacing (4.5 mm) and alignment along the rotating shaft. Operational parameters were optimized through kinematic analysis. A rotational speed of 245 rpm was selected to achieve a blade tip linear velocity of 1.8 m·s⁻¹—sufficient to induce rapid detachment while avoiding inertial damage. Structural integrity was evaluated via finite element analysis (FEA) in SolidWorks Simulation 2018. A high-density mesh (element size: 0.4 mm at stress concentration zones) was applied to the blade tip, with boundary conditions replicating the measured 0.46 N detachment force. Theoretical stress was calculated using Euler–Bernoulli beam theory for cantilevered loading. Field trials were conducted in a commercial saffron field (Khorasan, Iran) during peak bloom. Performance metrics included effective field capacity, percentage of damaged flowers (classified by stigma bruising, petal tearing, or stem bending), and adaptability to micro-terrain variations. A protective elastomeric layer (Shore A 70) was tested to assess its impact-dampening efficacy. Results and Discussion Quasi-static testing yielded a mean detachment force of 0.46 ± 0.08 N, consistent with the low tensile strength of saffron pedicel tissues under high moisture conditions. This low force threshold dictated the necessity of a controlled, non-impact harvesting mechanism. FEA of the blade under operational loading revealed a maximum von Mises stress of 104.1 MPa at the tip root—the critical failure location. Theoretical beam-bending analysis predicted a stress of 188.3 MPa, with the discrepancy attributed to idealized assumptions in the analytical model (e.g., perfect clamping, homogeneous material). Critically, both values remained well below the yield strength of AISI 304 (220.6 MPa), confirming a safety factor of ≥2.1 and eliminating the risk of plastic deformation during field operation (HassanBeigi et al., 2010). Field evaluations demonstrated an effective field capacity of 0.42 t·h⁻¹, markedly higher than the manual benchmark of 0.09 . The inclusion of an elastomeric protective layer reduced the percentage of damaged flowers from 23.7 ± 2.8% (bare metal configuration) to 8.2 ± 1.3%—a 65.4% reduction (p < 0.01, two-tailed t-test). Damage in the protected system was limited primarily to minor petal detachment, whereas the unprotected variant exhibited severe stigma bruising and style bending, directly impairing saffron quality. The sequential blade arrangement ensured uniform coverage across the row width (15 cm), eliminating flower retention in inter-blade zones—a common flaw in prior designs. These results confirm that successful saffron mechanization hinges not on brute-force automation, but on biomechanical fidelity: the precise matching of tool dynamics to plant structural response. The concave blade edge, optimized tip velocity, and elastomeric interface collectively replicate the dexterity of manual picking while offering scalable throughput. Conclusion This study demonstrates that a scientifically informed, plant-centric approach to tool design can overcome the longstanding barriers to saffron harvesting mechanization. By integrating empirical biomechanical data—particularly the low detachment force (0.46 N) and high tissue moisture—into the geometric and material configuration of a specialized harvesting blade, we achieved a system that simultaneously ensures flower integrity, mechanical reliability, and field-level efficiency.The blade’s stress response (104.1 MPa) remains safely within elastic limits, validating the structural design under real operational loads. The 65.4% reduction in floral damage through elastomeric protection underscores the critical role of contact surface engineering in preserving stigma quality. Furthermore, the 4.7-fold increase in field capacity over manual methods highlights the system’s potential to alleviate labor shortages and reduce occupational health risks. This work establishes a transferable framework for the mechanization of high-value, mechanically sensitive crops: one that prioritizes biological compatibility over mechanical dominance. Future efforts will focus on scaling the prototype to multi-row configurations and integrating real-time vision systems for selective harvesting.Introduction Saffron (Crocus sativus L.), renowned as “red gold,” is among the world’s most valuable agricultural commodities, valued for its unique coloring, aromatic, and pharmacological properties. Iran, contributing over 90% of global production, remains heavily dependent on manual harvesting—a labor-intensive practice that imposes severe ergonomic burdens on workers, particularly repetitive stress injuries to the lumbar spine and knee joints due to prolonged bending and squatting. The harvesting window is critically narrow, typically confined to 7–10 days of synchronous blooming, during which flowers must be collected at dawn to preserve stigma quality. This time sensitivity, combined with the extreme fragility of the stigma-pistil complex, renders the process highly susceptible to quality degradation when performed manually. Despite the widespread mechanization of major field crops, saffron harvesting has resisted automation due to three primary challenges: the delicate morphological structure of the flower, comprising three stigmas, three stamens, and six petals supported by a slender pedicel (~2 mm diameter); (ii) the high risk of mechanical damage during detachment, which directly compromises the economic value of the stigmas; and (iii) field heterogeneity, including uneven terrain, variable plant density, and inconsistent flower height. Existing prototypes—such as rotary rollers, pneumatic suction systems, and multi-tine pickers—have generally failed under real-world conditions, either causing excessive floral injury or requiring impractical power inputs. Recent research into aerodynamic separation and electrostatic harvesting has shown promise, yet these approaches often neglect the biomechanical response of saffron tissues under dynamic loading. To bridge this gap, this study proposes a biomimetic harvesting mechanism inspired by the precision of manual picking. A dedicated detachment blade was developed based on empirical characterization of saffron’s physical and mechanical properties. The system was evaluated under actual field conditions with respect to harvesting efficiency, flower integrity, adaptability to terrain irregularities, and mechanical reliability. The overarching objective is to establish a scientifically grounded foundation for semi-mechanized saffron harvesting that harmonizes plant-specific fragility with engineering robustness. Materials and Methods Saffron flowers consist of a central pistil bearing three vivid red stigmas—the sole economically valuable component—surrounded by three yellow stamens and six violet petals. To inform the design of a compatible harvesting tool, key physical and biomechanical properties were quantified under field-moist conditions. Moisture content was determined using the oven-drying method yielding values of 89.5% (w.b.) in stems and 78.2% in petals. Stem diameter was measured at the detachment zone (pedicel base) using a digital caliper (±0.01 mm), resulting in an average of 2.1 ± 0.3 mm. The force required for floral detachment was assessed via quasi-static uniaxial tensile tests conducted with a universal testing machine (Instron 3345, 10 N load cell, crosshead speed: 5 mm·min⁻¹). Flowers were gripped at the stigma base, and force was applied vertically until separation occurred. The detachment force was defined as the peak load preceding complete rupture. Based on these data, a harvesting blade was fabricated from austenitic stainless steel (AISI 304, yield strength = 220.6 MPa) using precision laser cutting (kerf width: 0.2 mm). The blade geometry featured a concave cutting edge designed to cradle the stem and guide it into the shear zone, minimizing lateral displacement and ensuring clean severance below the stigma attachment point. A polylactic acid (PLA) spacer, 3D-printed with 0.1 mm layer resolution, maintained uniform inter-blade spacing (4.5 mm) and alignment along the rotating shaft. Operational parameters were optimized through kinematic analysis. A rotational speed of 245 rpm was selected to achieve a blade tip linear velocity of 1.8 m·s⁻¹—sufficient to induce rapid detachment while avoiding inertial damage. Structural integrity was evaluated via finite element analysis (FEA) in SolidWorks Simulation 2018. A high-density mesh (element size: 0.4 mm at stress concentration zones) was applied to the blade tip, with boundary conditions replicating the measured 0.46 N detachment force. Theoretical stress was calculated using Euler–Bernoulli beam theory for cantilevered loading. Field trials were conducted in a commercial saffron field (Khorasan, Iran) during peak bloom. Performance metrics included effective field capacity, percentage of damaged flowers (classified by stigma bruising, petal tearing, or stem bending), and adaptability to micro-terrain variations. A protective elastomeric layer (Shore A 70) was tested to assess its impact-dampening efficacy. Results and Discussion Quasi-static testing yielded a mean detachment force of 0.46 ± 0.08 N, consistent with the low tensile strength of saffron pedicel tissues under high moisture conditions. This low force threshold dictated the necessity of a controlled, non-impact harvesting mechanism. FEA of the blade under operational loading revealed a maximum von Mises stress of 104.1 MPa at the tip root—the critical failure location. Theoretical beam-bending analysis predicted a stress of 188.3 MPa, with the discrepancy attributed to idealized assumptions in the analytical model (e.g., perfect clamping, homogeneous material). Critically, both values remained well below the yield strength of AISI 304 (220.6 MPa), confirming a safety factor of ≥2.1 and eliminating the risk of plastic deformation during field operation (HassanBeigi et al., 2010). Field evaluations demonstrated an effective field capacity of 0.42 t·h⁻¹, markedly higher than the manual benchmark of 0.09 . The inclusion of an elastomeric protective layer reduced the percentage of damaged flowers from 23.7 ± 2.8% (bare metal configuration) to 8.2 ± 1.3%—a 65.4% reduction (p < 0.01, two-tailed t-test). Damage in the protected system was limited primarily to minor petal detachment, whereas the unprotected variant exhibited severe stigma bruising and style bending, directly impairing saffron quality. The sequential blade arrangement ensured uniform coverage across the row width (15 cm), eliminating flower retention in inter-blade zones—a common flaw in prior designs. These results confirm that successful saffron mechanization hinges not on brute-force automation, but on biomechanical fidelity: the precise matching of tool dynamics to plant structural response. The concave blade edge, optimized tip velocity, and elastomeric interface collectively replicate the dexterity of manual picking while offering scalable throughput. Conclusion This study demonstrates that a scientifically informed, plant-centric approach to tool design can overcome the longstanding barriers to saffron harvesting mechanization. By integrating empirical biomechanical data—particularly the low detachment force (0.46 N) and high tissue moisture—into the geometric and material configuration of a specialized harvesting blade, we achieved a system that simultaneously ensures flower integrity, mechanical reliability, and field-level efficiency. The blade’s stress response (104.1 MPa) remains safely within elastic limits, validating the structural design under real operational loads. The 65.4% reduction in floral damage through elastomeric protection underscores the critical role of contact surface engineering in preserving stigma quality. Furthermore, the 4.7-fold increase in field capacity over manual methods highlights the system’s potential to alleviate labor shortages and reduce occupational health risks. This work establishes a transferable framework for the mechanization of high-value, mechanically sensitive crops: one that prioritizes biological compatibility over mechanical dominance. Future efforts will focus on scaling the prototype to multi-row configurations and integrating real-time vision systems for selective harvesting.

https://jam.tabrizu.ac.ir/article_21356_56c153f3e7ad78bff75da91ba0af4e1e.pdf

University of Tabriz Journal of Agricultural Mechanization 2383-126X 10 4 2025 12 22 The Effect of Anaerobic Granulobacter Microorganisms on the Process of Biogas Production from Urban Organic Waste (A Bench- Scale Study) The Effect of Anaerobic Granulobacter Microorganisms on the Process of Biogas Production from Urban Organic Waste (A Bench- Scale Study) 57 70 21357 10.22034/jam.2026.70459.1347 FA Leila Yousefi Department of Chemistry, Isl.C., Islamic Azad University, Islamshahr, Iran. Zahra Sadeghi Department of Chemical Engineering, SR.C., Islamic Azad University, Tehran, Iran. Sima Askari Department of Chemical Engineering, SR.C., Islamic Azad University, Tehran, Iran. Journal Article 2025 11 30 Introduction Organic waste is a significant problem in most countries around the world, including Iran, and every year large sums of public money expenditure are spent on its transportation, burial, and processing to mitigate in order to prevent environmental pollution and health issues risks. There are various methods for collecting and managing the management of organic waste, including waste incineration, as well as aerobic and anaerobic digestion incinerators, aerobic and anaerobic digesters. Biogas is one of the most promising bioenergy options among for non-fossil fuel-based energies, and it is noteworthy that a wide range of many biodegradable organic wastes, such as plant and animal matter organic matter, can serve as substrates for biogas production to urban waste water and some industrial waters, can be used as substrates for biogas production, provided that the necessary chemical and physical conditions for the growth of methane-producing bacteria archaea are established provided. The efficiency quality of the anaerobic sludge decomposition process under anaerobic conditions depends on environmental conditions and the microbial community mechanism of bacteria, so changes in operating conditions that lead to changes in the dominant bacterial species can significantly impact affect the performance of the digester. In this bench-scale study, anaerobic digestion was evaluated with different ratios amounts of feedstock feed, water, and Granulobacter inoculum Granobacteria was investigated with the aim of evaluating its potential using this type of bacteria as an bioaugmentation agent inoculant to increase the efficiency of the anaerobic digestion process on a laboratory bench scale. Materials and Methods The raw materials used in the experiment included urban waste (e.g., bread, orange peels, vegetables, egg cartons, fruit peels, rice, meat, eggshells, pasta, tea, and onion peels), Granulobacter, and sodium hydrogen carbonate (NaHCO3). The treatments consisted of 3033.20 g household waste + 3033.20 g water + 709.30 g Granulobacter (T1), 3972.70 g household waste + 3972.70 g water + 400 g Granulobacter (T2), 2415.30 g household waste + 2415.30 g water + 209.10 g Granulobacter (T3), and 2000 g household waste + 2000 g water + 200 g Granulobacter (T4). In each treatment, the primary feed sample (urban waste) was crushed into smaller pieces (less than 1 cm) and thoroughly mixed. An equal amount of water was then added, followed by adding Granulobacter to the feed. The pH of the feed was measured using a pH meter. Then, each treatment was poured into the digester tank, and the system was initiated. At the end of the digestion process, the biogas tank was separated from the system, and the gas contents were analyzed using gas chromatography (GC). Then, following the complete discharge of the biogas, the digester door was opened, and the remaining contents were subjected to elemental analysis, similar to the initial feed, as well as physicochemical tests (including dry matter, ash, and organic matter). Changes in pH, temperature, and pressure were measured throughout the process and compared across treatments. Data were analyzed using a factorial experiment in a completely randomized design with three replications. Mean comparisons were performed using Duncan's multiple range test at a probability level of α = 5 % using SPSS software (version 18). Results and Discussion The findings revealed that the moisture content of the digested samples increased compared to that of the initial feedstock. Moreover, in all treatments except T4, the ash content of the transformed feedstock during digestion was higher than that of the digested material. The largest reduction in carbon relative to the feedstock (1.80 %) was observed in T1, while the maximum methane content (43 %) was obtained in T2. Additionally, the pH reached approximately 6.75 in T1 after 90 days. However, it reached 7.45, 7.00, and 5.10 in T2, T3, and T4 after 49, 54, and 47 days, respectively. During the anaerobic digestion period, T1 showed low temperature fluctuations, maintaining a steady temperature of around 48 °C until the end of the period. In T2, the temperature declined from 50 °C on day 5 to 37 °C at the end of the period, whereas in T3, it rose by about 10 °C from day 5 to the end of the period. Notably, T4 showed temperature fluctuations within the range of 40–45 °C. Furthermore, reactor pressure fluctuations in T1 varied between 0.12 and 0.50 bar. In T2, the pressure varied between 0.15 and 0.25 bar from day 4 to the end of the period, while in T3, it ranged from 0.20 to 0.25 bar. In T4, the pressure remained almost constant (0.15 bar) throughout the entire period. Conclusion Anaerobic digestion is a biological process in which the organic matter is decomposed in the absence of oxygen through the participation of various bacterial species. In this study, the anaerobic digestion process was examined using different amounts of municipal organic waste, water, and Granulobacter. The results demonstrated that Granulobacter, when used as an inoculant, is a promising bacterium for increasing the efficiency of the anaerobic digestion process on a laboratory scale. Introduction Organic waste is a significant problem in most countries around the world, including Iran, and every year large sums of public money expenditure are spent on its transportation, burial, and processing to mitigate in order to prevent environmental pollution and health issues risks. There are various methods for collecting and managing the management of organic waste, including waste incineration, as well as aerobic and anaerobic digestion incinerators, aerobic and anaerobic digesters. Biogas is one of the most promising bioenergy options among for non-fossil fuel-based energies, and it is noteworthy that a wide range of many biodegradable organic wastes, such as plant and animal matter organic matter, can serve as substrates for biogas production to urban waste water and some industrial waters, can be used as substrates for biogas production, provided that the necessary chemical and physical conditions for the growth of methane-producing bacteria archaea are established provided. The efficiency quality of the anaerobic sludge decomposition process under anaerobic conditions depends on environmental conditions and the microbial community mechanism of bacteria, so changes in operating conditions that lead to changes in the dominant bacterial species can significantly impact affect the performance of the digester. In this bench-scale study, anaerobic digestion was evaluated with different ratios amounts of feedstock feed, water, and Granulobacter inoculum Granobacteria was investigated with the aim of evaluating its potential using this type of bacteria as an bioaugmentation agent inoculant to increase the efficiency of the anaerobic digestion process on a laboratory bench scale. Materials and Methods The raw materials used in the experiment included urban waste (e.g., bread, orange peels, vegetables, egg cartons, fruit peels, rice, meat, eggshells, pasta, tea, and onion peels), Granulobacter, and sodium hydrogen carbonate (NaHCO3). The treatments consisted of 3033.20 g household waste + 3033.20 g water + 709.30 g Granulobacter (T1), 3972.70 g household waste + 3972.70 g water + 400 g Granulobacter (T2), 2415.30 g household waste + 2415.30 g water + 209.10 g Granulobacter (T3), and 2000 g household waste + 2000 g water + 200 g Granulobacter (T4). In each treatment, the primary feed sample (urban waste) was crushed into smaller pieces (less than 1 cm) and thoroughly mixed. An equal amount of water was then added, followed by adding Granulobacter to the feed. The pH of the feed was measured using a pH meter. Then, each treatment was poured into the digester tank, and the system was initiated. At the end of the digestion process, the biogas tank was separated from the system, and the gas contents were analyzed using gas chromatography (GC). Then, following the complete discharge of the biogas, the digester door was opened, and the remaining contents were subjected to elemental analysis, similar to the initial feed, as well as physicochemical tests (including dry matter, ash, and organic matter). Changes in pH, temperature, and pressure were measured throughout the process and compared across treatments. Data were analyzed using a factorial experiment in a completely randomized design with three replications. Mean comparisons were performed using Duncan's multiple range test at a probability level of α = 5 % using SPSS software (version 18). Results and Discussion The findings revealed that the moisture content of the digested samples increased compared to that of the initial feedstock. Moreover, in all treatments except T4, the ash content of the transformed feedstock during digestion was higher than that of the digested material. The largest reduction in carbon relative to the feedstock (1.80 %) was observed in T1, while the maximum methane content (43 %) was obtained in T2. Additionally, the pH reached approximately 6.75 in T1 after 90 days. However, it reached 7.45, 7.00, and 5.10 in T2, T3, and T4 after 49, 54, and 47 days, respectively. During the anaerobic digestion period, T1 showed low temperature fluctuations, maintaining a steady temperature of around 48 °C until the end of the period. In T2, the temperature declined from 50 °C on day 5 to 37 °C at the end of the period, whereas in T3, it rose by about 10 °C from day 5 to the end of the period. Notably, T4 showed temperature fluctuations within the range of 40–45 °C. Furthermore, reactor pressure fluctuations in T1 varied between 0.12 and 0.50 bar. In T2, the pressure varied between 0.15 and 0.25 bar from day 4 to the end of the period, while in T3, it ranged from 0.20 to 0.25 bar. In T4, the pressure remained almost constant (0.15 bar) throughout the entire period. Conclusion Anaerobic digestion is a biological process in which the organic matter is decomposed in the absence of oxygen through the participation of various bacterial species. In this study, the anaerobic digestion process was examined using different amounts of municipal organic waste, water, and Granulobacter. The results demonstrated that Granulobacter, when used as an inoculant, is a promising bacterium for increasing the efficiency of the anaerobic digestion process on a laboratory scale.

https://jam.tabrizu.ac.ir/article_21357_22807cbcf18d6960f77604005c674409.pdf

University of Tabriz Journal of Agricultural Mechanization 2383-126X 10 4 2025 12 22 Evaluation of Diesel Engine Performance Efficiency with Biodiesel Fuel Derived from Linseed Evaluation of Diesel Engine Performance Efficiency with Biodiesel Fuel Derived from Linseed 71 85 21355 10.22034/jam.2026.66974.1324 FA Farid Moradi Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran Tarahom Mesri Gundoshmian Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran 0000-0002-7302-7269 Sajad Jafarzadeh Department of Biosystems Engineering, University of Mohaghegh Ardabili, Ardabil, Iran Journal Article 2025 05 08 Introduction Energy is a fundamental driver of economic development worldwide. Currently, approximately 80–90% of global energy is supplied by fossil fuels, which face increasing challenges including resource depletion and environmental degradation. In response, research efforts have shifted toward developing renewable alternatives such as biodiesel. This study examines the impact of biodiesel produced from linseed oil on the performance of tractor engines, addressing the urgent need for sustainable fuel solutions in agricultural machinery that maintain operational efficiency while reducing environmental impact. Materials and Methods Linseed oil was extracted from local varieties in Khalkhal County using n-hexane as a solvent in a Soxhlet apparatus, yielding 33% oil. The oil was then converted to biodiesel via a transesterification reaction, and its physicochemical properties were analyzed according to the ASTM D-6751 standard. Engine performance was evaluated on a TYM tractor engine across seven speed levels (380 to 620 rpm) using four fuel blends (0%, 5%, 10%, and 25% biodiesel). A Sigma 5 dynamometer was employed to measure torque and power output, while the specific fuel consumption (SFC) was calculated from the fuel consumption rate and the measured power. Results and Discussion The results indicated a statistically significant relationship between engine speed and torque output (P < 0.0001). Maximum torque (563.67 Nm) was recorded at 380 rpm, while minimum torque (165.17 Nm) occurred at 620 rpm. The use of biodiesel blends led to a slight reduction in torque, decreasing from 472.9 Nm with pure diesel to 466.72 Nm with the B25 blend—a trend attributable to the lower calorific value of biodiesel. Peak power output (29.24 kW) was observed at 580 rpm. Biodiesel blends caused a minor but consistent decrease in power across all tested speeds. Specific fuel consumption (SFC) increased with rising engine speed; however, biodiesel blends significantly improved fuel efficiency, likely due to enhanced combustion efficiency and higher oxygen content. ANOVA results showed no significant interaction between engine speed and fuel type for torque and power (P = 0.9996), but a significant interaction was observed for SFC (P = 0.031). Conclusion The produced linseed biodiesel complied with the ASTM D6751 standard, confirming its viability as an alternative fuel for diesel engines. Although slight reductions in torque and power output were observed—attributable to biodiesel's lower energy content—the fuel demonstrated improved consumption characteristics. This study suggests that linseed biodiesel can be effectively utilized in tractor engines, provided optimal blend ratios and operating conditions are carefully selected. Future research should focus on optimizing blend formulations and investigating emission profiles to further validate the practical application of linseed biodiesel in agricultural machinery. Introduction Energy is a fundamental driver of economic development worldwide. Currently, approximately 80–90% of global energy is supplied by fossil fuels, which face increasing challenges including resource depletion and environmental degradation. In response, research efforts have shifted toward developing renewable alternatives such as biodiesel. This study examines the impact of biodiesel produced from linseed oil on the performance of tractor engines, addressing the urgent need for sustainable fuel solutions in agricultural machinery that maintain operational efficiency while reducing environmental impact. Materials and Methods Linseed oil was extracted from local varieties in Khalkhal County using n-hexane as a solvent in a Soxhlet apparatus, yielding 33% oil. The oil was then converted to biodiesel via a transesterification reaction, and its physicochemical properties were analyzed according to the ASTM D-6751 standard. Engine performance was evaluated on a TYM tractor engine across seven speed levels (380 to 620 rpm) using four fuel blends (0%, 5%, 10%, and 25% biodiesel). A Sigma 5 dynamometer was employed to measure torque and power output, while the specific fuel consumption (SFC) was calculated from the fuel consumption rate and the measured power. Results and Discussion The results indicated a statistically significant relationship between engine speed and torque output (P < 0.0001). Maximum torque (563.67 Nm) was recorded at 380 rpm, while minimum torque (165.17 Nm) occurred at 620 rpm. The use of biodiesel blends led to a slight reduction in torque, decreasing from 472.9 Nm with pure diesel to 466.72 Nm with the B25 blend—a trend attributable to the lower calorific value of biodiesel. Peak power output (29.24 kW) was observed at 580 rpm. Biodiesel blends caused a minor but consistent decrease in power across all tested speeds. Specific fuel consumption (SFC) increased with rising engine speed; however, biodiesel blends significantly improved fuel efficiency, likely due to enhanced combustion efficiency and higher oxygen content. ANOVA results showed no significant interaction between engine speed and fuel type for torque and power (P = 0.9996), but a significant interaction was observed for SFC (P = 0.031). Conclusion The produced linseed biodiesel complied with the ASTM D6751 standard, confirming its viability as an alternative fuel for diesel engines. Although slight reductions in torque and power output were observed—attributable to biodiesel's lower energy content—the fuel demonstrated improved consumption characteristics. This study suggests that linseed biodiesel can be effectively utilized in tractor engines, provided optimal blend ratios and operating conditions are carefully selected. Future research should focus on optimizing blend formulations and investigating emission profiles to further validate the practical application of linseed biodiesel in agricultural machinery.

https://jam.tabrizu.ac.ir/article_21355_f4a84cbcd16e5ee3af2677a2cecea048.pdf