https://jam.tabrizu.ac.ir/ Journal of Agricultural Mechanization en daily 1 Mon, 22 Dec 2025 00:00:00 +0330 Mon, 22 Dec 2025 00:00:00 +0330 https://jam.tabrizu.ac.ir/article_20987.html IntroductionAgricultural 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 MethodsThe 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 DiscussionThe experimental results revealed that the pneumatic system significantly outperformed the conventional hydraulic and mechanical systems across all major performance parameters.Energy ConsumptionThe 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 TimeThe 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 UniformityThreshing 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 ReliabilityDurability 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 EvaluationThe 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 VariablesANOVA 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 InsightsDynamic 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. ConclusionThis 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_21672.html The optimum consumption of energy is one the sustainable production factors of agricultural crops. Enhancement of production will be sustainable when the use of energy is on the optimum level. The objective of this study was to assessment of energy consumption of three strategic crops including wheat, corn and sugar beet in Tehran, Alborz and Ghazvin provinces. Ten farmers have been interviewed face to face in this study. Fuel consumption measured for different agricultural operation. Results indicated that input energy of three provinces for corn was 71039.47 MJ ha-1. The average of output energy for corn was 131041.83 MJ ha-1. Maximum and minimum of consumed input energy in corn production was allocated to electricity with 56.78 % and labor with 0.24 % of the total input energy. Energy efficiency of corn obtained 1.84. Energy productivity of corn was determined 0.13 kg MJ-1. Net energy was calculated 60002.35 MJ ha-1 and energy intensity of those crops was determined 7.97 MJ kg-1. IntroductionThe agricultural sector, as the primary producer of the nation’s food supply, is not only a major consumer of energy but also an important provider of energy resources. In addition to technical analyses, economic, energy, and environmental assessments are crucial components in evaluating agricultural projects. Agricultural production requires energy derived from various sources. The cultivation of agricultural products demands substantial amounts of human, animal, chemical, and fossil energy inputs. Therefore, energy plays a critical role in the development and efficiency of the agricultural sector. Energy index analysis is a key necessity in agriculture. Through analyzing energy consumption patterns, strategies can be proposed to optimize energy use, minimize unnecessary losses, and enhance productivity and profitability. Considering the diverse energy use areas within agriculture, effective energy management can significantly improve resource efficiency. The main stages of energy management include controlling energy consumption, investing in energy-saving technologies, and maintaining and preserving energy resources. The objective of this study was to evaluate the energy consumption associated with corn production in the provinces of Tehran, Alborz, and Qazvin.Materials and MethodsIn this study, face-to-face interviews were conducted with 11 corn producers, and field observations were carried out to measure the amount of fuel consumption for various agricultural operations. Initially, a questionnaire was designed based on the opinions of agricultural experts from the Agricultural Jihad Organization and several leading farmers in the study area to obtain comprehensive data. The first two sections of the questionnaire contained general information about the farmer and the farm, such as total cultivated area, seed type, farming experience, land ownership, and agricultural machinery. Subsequent sections of the questionnaire covered all stages of corn production, including land preparation and tillage, planting, weeding, irrigation, pest control, fertilization, and harvesting operations. Each section gathered information on the quantity of inputs used, the methods and frequency of various agricultural activities, and data on input costs and revenues. The final part of the questionnaire included information related to additional activities such as field supervision, guarding, and other miscellaneous inputs. Moreover, data on the previous crop cultivated before corn were also collected. After designing and completing the questionnaires with farmers across the three provinces, the collected data were carefully evaluated and analyzed. During questionnaire design, special attention was given to the simplicity and clarity of questions. The questionnaire’s validity was further confirmed based on the opinions of university professors, agricultural experts, and results from a pilot test. A simple random sampling method was used, ensuring that the obtained results were reliable and generalizable to the entire population. To determine the total energy inputs used in corn production, the equivalent energy values of electricity, fuel, seed, machinery, human labor, fertilizers, and pesticides were calculated, and their shares in total energy consumption were determined. Results and DiscussionThe results indicated that in the studied provinces, the average total input energy for corn production was 71,039.47 MJ ha⁻¹, while the average corn yield across Tehran, Alborz, and Qazvin provinces was 8,914.41 kg ha⁻¹. The total output energy obtained from corn production in these provinces was calculated as 131,041.83 MJ ha⁻¹. In comparison, previous studies have reported an average yield of 6,167 kg ha⁻¹ for corn production. Among the input energy sources, electricity accounted for the highest share of total input energy, contributing 56.78%, while human labor energy had the lowest share with 0.24% of total input energy. Based on the calculated energy indices for corn production: Energy efficiency (energy ratio):1.84, Energy productivity:0.13 kg MJ⁻¹, Net energy:60,002.35 MJ ha⁻¹, Energy intensity:7.97 MJ kg⁻¹. ConclusionThe results showed that the average total input energy for corn production in the provinces of Tehran, Alborz, and Qazvin was 71,039.47 MJ ha⁻¹, while the average total output energy was 131,041.83 MJ ha⁻¹. The highest share of input energy consumption was related to electricity, with 40,335.12 MJ ha⁻¹, accounting for 56.78%of total input energy. This was followed by nitrogen fertilizer, with 12,930.37 MJ ha⁻¹, representing 18.2%of total input energy. In contrast, human labor energy had the lowest share, amounting to 171.3 MJ ha⁻¹(i.e., 0.24%of total input energy). The total direct and indirect energy inputs in corn production for the studied provinces were 49,072.18 MJ ha⁻¹and 21,967.29 MJ ha⁻¹, respectively. Furthermore, renewable and non-renewable energy inputs were estimated at 43,066.42 MJ ha⁻¹and 27,973.05 MJ ha⁻¹, respectively. The average energy indices for corn production in Tehran, Alborz, and Qazvin provinces were calculated as follows: Energy use efficiency (energy ratio):1.84, Energy productivity:0.13 kg MJ⁻¹, Net energy:60,002.35 MJ ha⁻¹, Energy intensity:7.97 MJ kg⁻¹ Overall. these results indicate that electricity and nitrogen fertilizer are the most energy-demanding inputs in corn production, highlighting the potential for improving energy efficiency through better management of electrical and fertilizer use. https://jam.tabrizu.ac.ir/article_20988.html 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.IntroductionThe 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 MethodsTo 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 DiscussionThe 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.ConclusionThis 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.IntroductionThe 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 MethodsTo 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 DiscussionThe 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.ConclusionThis 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_21690.html The high viscosity and inherent stickiness of honey pose substantial challenges for handling, transport, packaging, and its incorporation as a food ingredient. Converting honey into a powder can mitigate many of these constraints and broaden its practical applications. This study aimed to produce honey powder via spray drying and to characterize the physicochemical quality of the resulting product. Spray drying was conducted at three inlet air temperatures (120, 150, and 180 °C), three atomization pressures (0.5, 1.0, and 1.5 bar), and three maltodextrin carrier levels (15, 32.5, and 50% w/w, based on honey dry matter). Process conditions were optimized using response surface methodology, with particular emphasis on atomization pressure as a key operating variable. The powder was evaluated for moisture content, water activity, bulk density, flowability, color parameters, pH, sucrose content, fructose-to-glucose ratio, and hydroxymethylfurfural (HMF) concentration. Analysis of variance indicated that inlet air temperature and maltodextrin concentration significantly affected (p < 0.05) most quality attributes. Temperature influenced 9 of the 11 measured responses and was the dominant factor overall, with pronounced effects on moisture, water activity, bulk density, flowability, HMF formation, and color indices. Maltodextrin concentration significantly affected water activity, pH, sucrose content, fructose-to-glucose ratio, and yellowness. Atomization pressure also contributed significantly to several critical responses, including moisture content, bulk density, water activity, yellowness, and HMF concentration. Multi-response optimization identified optimal conditions at an inlet air temperature of 170–180 °C, atomization pressure of 1.0–1.5 bar, and maltodextrin concentration of 45–50%. Under these settings, the process yielded honey powder with moisture content below 2%, water activity below 0.30, high flowability, desirable lightness, and low HMF levels.Introduction:Honey is a high-viscosity, hygroscopic natural sweetener, and its inherent stickiness complicates pumping, storage, transportation, and incorporation into food formulations. Converting honey into a powdered form offers clear functional benefits, including improved flowability, reduced tackiness, extended shelf life, easier handling, and broader applicability in dry food systems. Among available technologies, spray drying is considered particularly suitable for producing honey powder because it enables rapid moisture removal through efficient heat and mass transfer while helping to preserve product quality.Despite these advantages, spray-drying honey remains technically challenging. Honey’s high sugar content, low glass transition temperature, and pronounced hygroscopicity promote stickiness, wall deposition, and agglomeration during drying, which can reduce powder yield and compromise quality. To mitigate these issues, carrier materials—most commonly maltodextrin are routinely added to increase the glass transition temperature, reduce stickiness, and improve powder stability.While prior studies have largely focused on the influence of inlet air temperature and carrier concentration, the effect of atomizer pressure has received comparatively limited attention. Atomizer pressure is a critical operational variable because it governs droplet size distribution, drying kinetics, and particle morphology, which in turn affect key quality attributes of the resulting powder. Accordingly, the present study addresses this gap by examining the combined effects of inlet temperature, maltodextrin concentration, and atomizer pressure on the physicochemical properties of spray-dried honey powder.Materials and Methods: Pure honey (Azar Kando; Golshad Co., Iran) was blended with maltodextrin at three carrier levels (15, 32.5, and 50% w/w, based on honey dry matter). Spray drying was performed using a laboratory-scale spray dryer (Dorsa Behsaz, Iran) fitted with a cylindrical cyclone chamber (65 × 43 × 110 cm).Process optimization was conducted using a Box–Behnken response surface methodology (RSM) design with three independent variables: inlet air temperature (120, 150, and 180 °C), atomizer pressure (0.5, 1.0, and 1.5 bar), and maltodextrin concentration (15, 32.5, and 50%). In total, 17 experimental runs were carried out according to the experimental design. The resulting powders were collected and stored at 4 ± 1 °C until further analyses.Response variables included moisture content, water activity, bulk density, flowability (Carr Index and Hausner Ratio), pH, sucrose content, fructose-to-glucose (F/G) ratio, hydroxymethylfurfural (HMF) concentration, and color attributes (L*, a*, and b*). All measurements were performed in accordance with AOAC procedures and relevant Iranian national standards.The statistical analysis confirmed the adequacy of the fitted RSM models, as indicated by high coefficients of determination and non-significant lack-of-fit values. Among the investigated variables, inlet air temperature was identified as the most influential factor, significantly affecting 9 out of the 11 measured quality attributes. Maltodextrin concentration also exerted a significant impact on several key parameters, while atomizer pressure, although less dominant overall, played a statistically meaningful role in determining critical quality characteristics.Moisture content and water activity decreased significantly with increasing inlet air temperature and maltodextrin concentration. Higher temperatures enhanced the driving force for moisture evaporation, while increased carrier content facilitated the formation of a protective matrix that reduced hygroscopicity. The lowest moisture content (below 2%) and water activity (below 0.30) were achieved at an inlet temperature of 180 °C, maltodextrin concentration of 50%, and atomizer pressure of 1.5 bar. Increasing atomizer pressure further contributed to reduced moisture levels, likely due to the formation of finer droplets with larger surface areas, leading to more efficient drying.Bulk density increased with increasing inlet temperature and maltodextrin concentration, which can be attributed to the formation of more compact and less porous particles. Under these same conditions, powder flowability improved significantly, as reflected by lower Carr Index and Hausner Ratio values. Higher atomizer pressure also enhanced flowability, probably by producing smaller and more uniform particles with reduced interparticle cohesion. The best flowability characteristics were observed at high temperature (180 °C), high maltodextrin content (50%), and an atomizer pressure of 1.5 bar.The pH of honey powder showed a slight increase with increasing inlet air temperature and maltodextrin concentration. This behavior may be explained by dilution of organic acids due to carrier addition and possible buffering effects of maltodextrin. Sucrose content increased at higher temperatures and carrier levels, suggesting improved sugar retention and reduced degradation under optimized drying conditions.The fructose-to-glucose (F/G) ratio decreased with increasing inlet air temperature and atomizer pressure, indicating a higher susceptibility of fructose to thermal degradation. In contrast, higher maltodextrin concentrations helped preserve fructose, resulting in higher F/G ratios. Maintaining an adequate F/G ratio is important for minimizing crystallization and caking during storage, and the most favorable preservation of this ratio was achieved at lower temperatures and pressures combined with higher carrier content.Hydroxymethylfurfural (HMF), a key indicator of thermal degradation and non-enzymatic browning in honey, increased significantly at inlet air temperatures above 150 °C. However, both maltodextrin addition and increased atomizer pressure were effective in mitigating HMF formation. Maltodextrin likely acted as a protective matrix, reducing the exposure of sugars to high temperatures, while higher atomizer pressure shortened residence time by accelerating moisture removal. Acceptable HMF levels (below the regulatory threshold of 40 ppm) were maintained at inlet temperatures of 150 °C or lower when combined with high maltodextrin content and atomizer pressures of at least 1 bar.Color analysis revealed that increasing inlet air temperature led to a decrease in lightness (L*) and an increase in redness (a*), reflecting intensified browning reactions. Conversely, maltodextrin significantly improved lightness, demonstrating its protective effect on color preservation. Atomizer pressure exerted a moderate influence on color attributes, possibly due to changes in particle structure and light-scattering behavior. The brightest powders with minimal browning were obtained at lower temperatures, lower atomizer pressure, and higher carrier concentration.One of the most notable findings of this study was the demonstrated importance of atomizer pressure as a key processing variable. While previous studies have primarily focused on inlet air temperature and carrier concentration, the present results highlight that atomizer pressure plays a crucial role in governing droplet size distribution, drying efficiency, particle morphology, and ultimately, powder quality. Increasing atomizer pressure from 0.5 to 1.5 bar improved powder homogeneity, reduced moisture content and water activity, enhanced flowability, and contributed to lower HMF formation.Multi-response optimization using the desirability function approach identified the optimal spray-drying conditions as an inlet air temperature of 170–180 °C, maltodextrin concentration of 45–50%, and atomizer pressure of 1.0–1.5 bar. Under these conditions, the produced honey powder exhibited moisture content below 2%, water activity below 0.30, excellent flowability, desirable lightness, and low HMF levels, satisfying key quality requirements for honey powder intended for food applications.This study shows that inlet air temperature, maltodextrin concentration, and atomizer pressure collectively govern the physicochemical and functional quality of spray-dried honey powder. Inlet temperature was the most influential parameter across the measured responses; however, atomizer pressure—an operating variable that has received limited attention in prior work—was also critical for regulating moisture content, water activity, flowability, and hydroxymethylfurfural (HMF) formation.Multi-response optimization identified the following conditions as optimal for producing high-quality honey powder: an inlet air temperature of 170–180 °C, maltodextrin concentration of 45–50%, and atomizer pressure of 1.0–1.5 bar. Under these settings, the resulting powder achieved moisture levels below 2%, water activity below 0.30, excellent flowability, high lightness, and acceptable HMF concentrations, meeting the relevant quality requirements for honey powder.Overall, the results underscore the need to explicitly incorporate atomizer pressure into future spray-drying optimization frameworks for honey and similarly heat-sensitive, sticky, and sugar-rich materials. https://jam.tabrizu.ac.ir/article_21296.html IntroductionThe 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 MethodsThis 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 DiscussionTraining-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.ConclusionThis 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 effectivelyFuture 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.AcknowledgementThe 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_21748.html Considering the rapid advancement of technology worldwide and the emergence of new innovations in agricultural machinery and precision agriculture—especially in recent years—along with the growing emphasis on energy savings and the rising cost of fuel globally, and particularly in Iran, improving the quality of tractors has become essential (Lanças et al., 2024). As the most important piece of agricultural machinery, tractors play a critical role in modern farming, and enhancing their efficiency and performance is vital for sustainable agricultural development (de Melo et al., 2022; Hoy & Kocher, 2020; Zhu et al., 2022).The purpose of this research is to investigate the effects of installing a turbocharger system on engine fuel consumption, power, slip, and traction parameters in a turbocharged ITM4120 tractor compared to a conventional ITM399 tractor without a turbocharger.Materials and MethodsThe tests were conducted on the second-grade concrete runway at the Tabriz Tractor Manufacturing Company for various tractor evaluations, including traction tests, in accordance with the guidelines provided by the Standards Organization. The air temperature was 23 ± 7 ℃, and the air pressure was approximately 96.6 kPa. The weather conditions ranged from partly cloudy to clear, as per OECD standards. To conduct the tests, ITM4120 and ITM399 tractors were used (Figure 1). To measure the traction force between the two tractors, a 5-ton load cell model 5BBP , manufactured by Bongshin Korea , was used. This device has a measurement accuracy of 0.1 kg . A Sigma 5 dynamometer made in England was used to measure PTO power. This dynamometer has a maximum operating speed of 100 km/h, a maximum coupling weight of 100 kg, and a maximum axle weight of 1300 kg, with a power measurement accuracy of 0.1 hp and torque of 0.1 rpm. Additionally, a VDO-EDM1404 fuel gauge (manufactured in Germany ) with a measurement accuracy of ±1% was employed to measure fuel consumption. A stopwatch with millisecond accuracy was also used for precise timing during the tests. Furthermore, a thermometer was utilized to measure the ambient air temperature and ensure it remained within the OECD standard range , with an accuracy of ±0.1 degrees Celsius . (Figure 2). The specifications of the ITM 399 and ITM 4120 tractors are shown in Table 1.Preparation of test equipment and devicesAccording to the recommendations of the standards organization and the manufacturer of the tractor and tires used, prior to the commencement of the tests, eight suitcase-shaped weights, each weighing 34 kg, were permanently installed at the front of the tractor. Additionally, two cast-iron weights, each weighing 50 kg, were permanently installed on each of the rear wheels of the tractors. ballast, in this study refers to being the tires are filled with water or left empty. Tire ballast was applied to each tractor with an air pressure ranging from 0.8 to 1 bar. To increase tractive force while maintaining the center of gravity and ensuring appropriate weight distribution for four-wheel-drive tractors, the tires were filled with water, as outlined in Table 2.The traction test was conducted on four-wheel-drive tractors in this project. The test was performed in accordance with OECD standards, in light and heavy gears, as well as tortoise and rabbit modes, at varying engine speeds. The tests were repeated three times on the concrete runway of the Tractor Manufacturing Company. During these tests, parameters such as slip percentage, tractive force, power, fuel consumption, specific fuel consumption, and specific power were measured and calculated. These values were also computed with ballast applied at maximum power across different gears (gears one, two, and three in both rabbit and turtle modes, as well as in two modes—light and heavy—using a 12-gear synchronized lever). The tests were carried out under varying loads, corresponding to 25%, 50%, 75%, 85%, and 100% of the pulling force at maximum power, as well as 50% of the pulling force in the first lighter gear, where the engine speed drops in both tractors. The data values were recorded in accordance with the standard table for all three repetitions for each tractor. Subsequently, the necessary analyses were performed on the collected data.Drawbar tensile testTo conduct the tests, each of the tractors under examination (the turbocharged ITM4120 and the non-turbocharged ITM399) was hitched to the ITM1500 tractor, which was equipped with a throttle to generate drawbar pull as the load tractor. The tested tractors pulled the load tractor in different gears, while a load cell placed between the two tractors recorded the traction force. This data was logged by a data logger installed inside the cabin (Figure 3). These tests were performed for both tractors in three experimental stages. The wheel slip of the driving wheels of the test tractor, forward speed, and data from the load cell and fuel gauge were recorded in two conditions: with and without load, and with and without ballast.Results and DiscussionIn the results, the charts and tables related to the statistical analysis of data from the ITM4120 turbocharged tractor and the ITM399 non-turbocharged tractor's drawbar pull tests with ballast are presented. The effects of the turbocharger system on parameters such as drawbar pull force, fuel consumption, specific fuel consumption, and tractor power have been included. The statistical design employed utilized two-way ANOVA in the SAS software due to the presence of two independent variables (gear effect and tractor type). https://jam.tabrizu.ac.ir/article_21356.html 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.IntroductionSaffron (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 MethodsSaffron 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 DiscussionQuasi-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.ConclusionThis 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.IntroductionSaffron (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 MethodsSaffron 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 DiscussionQuasi-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.ConclusionThis 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_21750.html This study introduces a novel and integrated Internet of Things (IoT) framework designed to enable intelligent and scalable health monitoring of dairy cattle, combining low-power piezoelectric sensing with deep learning methodologies. The proposed system addresses key limitations in the field of precision livestock farming (PLF) by focusing on the automated recognition of critical bovine behaviors—specifically, Feeding and Rumination. Central to this research is a comprehensive comparative analysis between two signal processing approaches: time-domain feature extraction versus Discrete Wavelet Transform (DWT), aimed at optimizing the classification accuracy and computational performance of the monitoring pipeline.The implemented architecture is composed of four primary components: (1) piezoelectric sensors with a sampling rate of 24 Hz to capture jaw movements, (2) two parallel signal processing pipelines, (3) a wireless communication system based on LoRaWAN, and (4) a deep learning classifier built using convolutional neural networks (CNNs). The system was deployed and validated under real operating conditions at the dairy farm of the University of Tabriz.From a hardware perspective, the solution utilizes piezoelectric sensors mounted on bovine halters to detect jaw activity associated with feeding and rumination behaviors. These sensors are interfaced with ESP32 microcontrollers that process the signal in real-time and transmit it via Bluetooth to a custom-designed mobile application. Additionally, raw sensor data is uploaded to Google Drive to facilitate cloud-based storage and collaborative analysis. The data preprocessing stage was performed in Google Colab using Python, consisting of three critical steps: digital Butterworth filtering to eliminate ambient noise, min-max normalization to standardize amplitude variations, and segmentation of the continuous data into fixed-length time windows of 20 to 30 samples (corresponding to 0.83–1.25 seconds per segment).For feature extraction, two concurrent methodologies were applied. In the time-domain pathway, statistical metrics—including mean, standard deviation, minimum, and maximum values—were calculated. In parallel, the DWT approach utilized Daubechies wavelets to derive spectral-temporal coefficients from the signals, capturing transient patterns that may correspond to complex mastication phases. Given the class imbalance in the dataset—particularly a relative shortage of Feeding samples—the Synthetic Minority Over-sampling Technique (SMOTE) was used via the imbalanced-learn Python library to enhance classifier robustness and reduce bias.The deep learning model employed for classification was a 1D Convolutional Neural Network (1D-CNN) constructed in TensorFlow/Keras. The network included two convolutional blocks. The first block utilized 64 filters with a kernel size of 3, followed by batch normalization, Swish activation, and max pooling. The second block employed 32 filters with identical operations. After convolution, the network architecture incorporated a flattening layer, a dense layer with 128 units, dropout regularization (dropout rate of 0.5), and a final softmax layer for multi-class output. The training configuration used Adam optimization with an initial learning rate of 0.001, categorical cross-entropy loss, a batch size of 256, and a total of 200 training epochs. To prevent overfitting, early stopping with a patience of 10 epochs and dynamic learning rate reduction strategies were applied. Model evaluation relied on accuracy scores, F1-scores, and confusion matrices, visualized using Matplotlib and Seaborn libraries.Experimental results highlighted substantial differences in performance between the two signal processing approaches. The time-domain (non-DWT) method outperformed the DWT approach in several key areas. Specifically, the non-DWT model achieved a validation accuracy of 86.78%, compared to approximately 76% for the DWT model. In terms of computational efficiency, the non-DWT model trained in 21.68 minutes, whereas the DWT version required approximately 23 minutes. Additionally, the non-DWT model had a significantly leaner architecture, consisting of 0.17 million parameters, which is 39.3% fewer than the 0.28 million parameters in the DWT-based model.Class-specific evaluation revealed nuanced insights. While the DWT method demonstrated a modest 1.1% improvement in F1-score for Feeding behavior detection (0.7627 vs. 0.7520), likely due to better capture of spectral features during chewing cycles, the time-domain model excelled in Rumination detection with an F1-score of 0.9903 compared to 0.9888 from DWT—an advantage attributed to the periodic nature of rumination, making spectral analysis less necessary.Based on these findings, three key operational recommendations are proposed for real-world adoption in precision livestock farming. First, large-scale deployments should favor the non-DWT configuration due to its 8.3% faster training time, reduced computational complexity, and lower hardware requirements—beneficial for cost-sensitive and resource-limited environments. Second, selective use of DWT-based monitoring may be justified for high-risk subpopulations, such as peri-parturient cows vulnerable to metabolic conditions (e.g., ketosis or acidosis), where the slight improvement in Feeding behavior detection could have clinical value. Third, a hybrid system architecture—employing time-domain processing for Rumination and DWT for Feeding—could optimize both performance and resource allocation, ensuring critical diagnostic coverage.The LoRaWAN-enabled communication infrastructure ensures that alerts and behavioral reports can be transmitted with latency under two minutes, even across large-scale farm environments, highlighting the system’s practical feasibility.For future development, four promising research directions are identified. First, implementing edge computing by deploying optimized CNN models directly on ESP32 microcontrollers would eliminate reliance on cloud services, improving real-time responsiveness. Second, incorporating multi-sensor fusion—combining piezoelectric data with audio or accelerometer inputs—may further enhance classification accuracy, particularly for the underrepresented Feeding class. Third, adapting DWT-based feature extraction techniques to agricultural robotics (e.g., autonomous feeders) and remote sensing (e.g., pasture quality assessment) opens new application frontiers. Finally, reinforcement learning could be used to dynamically allocate sensing and processing resources based on individual animal risk profiles.In conclusion, the proposed system demonstrates that time-domain feature extraction strikes an effective balance between diagnostic accuracy (86.78%), computational efficiency (0.17M parameters), and scalability for large-scale deployment in dairy farming. While DWT methods offer marginal gains in specific use-cases, their higher complexity limits practicality. The presented IoT architecture delivers a viable solution for smart livestock management and establishes a foundation for next-generation advancements in agricultural automation and sustainability. https://jam.tabrizu.ac.ir/article_21357.html IntroductionOrganic 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 MethodsThe 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 DiscussionThe 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.ConclusionAnaerobic 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_21749.html The growing trend of the world population has brought about an inevitable increase in energy demand, and this issue, apart from the fact that non-renewable energy sources are finite, can bring about many environmental problems. Considering the importance of environmental impacts and the development of renewable energies, the construction of wind farms to absorb wind energy as one of the renewable energies is increasing all over the world. Wind is one of the important sources of energy that is clean, cheap, available and permanent. This article reviews wind power plants from their emergence and development and the research conducted in this field. One of the important factors is the economic analysis of the construction of a wind power plant, which is done by examining the investment and annual costs and the income from the power plant. Another important point in the construction of wind farms is the optimal arrangement of the farm, including the number of turbines and their arrangement, so that maximum energy production and efficiency are achieved with the lowest cost of connection between turbines. Finally, the environmental impacts of wind power plants were addressed, including the environmental impacts of electricity generation, the effects of wind turbine noise on noise annoyance, and the impact of wind power plants on air temperature.IntroductionOne of the solutions to energy shortages and dependence on non-renewable resources is to increase the use of renewable energy sources. In this regard, new energies, including hydropower, solar, and wind energies, are of particular importance because they do not pollute the environment. Wind energy has increased its presence in electrical power systems around the world in recent decades. Wind power plants have expanded more than any other existing technology for utilizing renewable resources in the power system due to their high efficiency, institutionalization of the technology used to exploit wind energy, low cost of electricity generation, presence of windy areas most of the time, and ability to generate power on a large scale and cost-effectively. On the other hand, the increasing technological progress of wind turbine manufacturing, which includes reducing the costs of designing and operating wind turbines, has forced countries around the world to use this energy more and more. Today, wind power (the conversion of wind energy into a useful form of energy such as electrical energy using wind turbines, mechanical energy, for example in windmills or wind pumps, or the propulsion of boats and ships, for example in sailboats) in the world has an annual production capacity of 430 TWh of electrical energy, which is 2.5% of the world's electricity consumption. The annual production capacity of electricity can be increased by expanding wind farms.Literature reviewBefore building a wind farm, a financial and economic assessment is essential and critical. The economic assessment should be considered from both a national perspective and a power producer perspective. In the national economic assessment, the benefits of the wind farm project are compared with the costs of an alternative thermal power plant (e.g., a gas turbine), including fuel, repair, and maintenance costs. In the power producer economic assessment, the revenues are related to the present value of the electricity sales revenue over the life of the project.One of the most important and complex issues in the construction of wind farms is the optimal number and arrangement of turbines in relation to each other in order to maximize energy production and efficiency. The arrangement and installation of turbines in the farm, given the limitations of land and capital, requires precise calculations in order to obtain the most energy from the power plant. The speed of the wind exiting the turbine decreases after passing through it and some of its energy is reduced. This phenomenon is called wake. This reduction in wind speed is a function of various factors such as distance, turbine dimensions and wind speed entering the turbine. Of course, this effect improves with distance, which is due to the general air currents in the area. Therefore, the denser the turbines in a power plant, the less wind is able to recover and as a result, the output power decreases.Environmental studies conducted on wind energy show that the turbine production and wind power plant construction stage is a significant factor in greenhouse gas emissions in wind power plants, accounting for about 73-90% of the cumulative greenhouse gas emissions, and the remaining stages, including operation and maintenance, power plant demolition, and transportation, account for about 10-90%. Wind power plants, as one of the new methods of producing renewable energy with the least environmental impact compared to other energy sources, are considered one of the sources of noise pollution that have long attracted the attention of many researchers. Studies have examined the potential impacts of wind farms on global and local weather and climate. Modeling studies agree that wind farms can significantly influence local meteorology. In some cases, these effects may be beneficial, such as nighttime warming in stable conditions that can protect crops from frost.ConclusionThis article aims to examine wind power plants and review the research conducted in this field. Wind is one of the clean, cheap, permanent and available renewable energy sources in the world. Many countries, including the United States and China, have made investments in this field and are making great use of this energy. Iran also has a number of wind farms, but as a country with many windy areas, if this energy source is used, it can provide a large part of its energy needs. There are important points to consider when constructing a wind power plant. The most important point is the economic analysis of constructing a wind power plant. At this stage, the investment and annual costs of the power plant are examined, and on the other hand, the income from the power plant, which includes the sale of electricity, is predicted. If it is economically viable, the wind power plant can be constructed. Another important point is the layout of the farm, which should consider the layout and number of turbines to produce the most energy for the least cost to connect the turbines. The next important point is the environmental impacts of wind power plants, both during the construction and operation stages, which should be minimized. These factors include the environmental impacts of electricity generation, the effects of wind turbine noise on noise nuisance, and the effects of the power plant on air temperature. https://jam.tabrizu.ac.ir/article_21355.html IntroductionEnergy 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 MethodsLinseed 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 DiscussionThe 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).ConclusionThe 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.