University of TabrizJournal of Agricultural Mechanization2383-126X10320251222Drying Kinetics and Mathematical Modeling of Orange Slices in Refractance Window Drying SystemDrying Kinetics and Mathematical Modeling of Orange Slices in Refractance Window Drying System1152034310.22034/jam.2025.67311.1325FABehzadBakhshiDepartment of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, IranMohammad RezaBayatiDepartment of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, IranRezaTabatabaeekoloorDepartment of Biosystem Mechanics, Faculty of Agricultural Engineering, Sari University of Agricultural Sciences and Natural ResourcesAbbasRohaniDepartment of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran0000-0002-4494-7058ElhamAzarpazhoohKhorasan Razavi Agricultural and Natural Resources Research and Education CenterJournal Article20250514In this study, the drying parameters and kinetics of sliced orange (Thomson variety) were investigated in a refractance window dryer at temperatures (60, 75, and 90 degrees Celsius) and slice thicknesses (4, 6, and 8 mm). Analysis of variance was used to determine the effects of temperature and thickness on the parameters of color change, shrinkage, and resorption intensity. Five mathematical models were selected to describe and compare the drying kinetics of orange slices, and the coefficient of determination (R<sup>2</sup>), chi-square (χ<sup>2</sup>), and root mean square error (RMSE) were used for evaluation. Also, the moisture transfer from orange slices was described by fitting the Fick diffusion model. The results showed that drying temperature and slice thickness had a significant effect on the drying behavior of orange slices. Drying time decreased with increasing temperature and decreasing thickness. Temperature and thickness had little effect on the total color changes, shrinkage rate, and resorption intensity of dried orange slices. Among the mathematical models, the modified Page model had the best fit with the lowest error and the highest coefficient of determination. The effective diffusion coefficient (D<sub>eff</sub>) increased with increasing drying temperature and was found to be in the range of 39.6× 10<sup>-10</sup> - 42.5× 10<sup>-10</sup> m<sup>2</sup>/s. The temperature dependence of the effective diffusion was described by the Arrhenius equation and the activation energy for moisture diffusion in orange slices was determined to be 27.5 kJ/mol..<br /><em>Introduction</em><br />Iran ranks seventh in the world in terms of citrus production. The country's annual citrus production is about six million tons, of which three million tons are oranges. A significant portion of the oranges produced, after consumption as fresh fruit and export, can be used in various processing and complementary industries, including fruit juice and dried fruit. Since fruit drying can be done on a small and home scale, the development of suitable dryers with high energy efficiency is very important. Refractance Window, known as the fourth generation of dryers, is a moisture removal system for producing high-quality dried or concentrated foods. Refractance window dryers can produce dried products with relatively low energy consumption, in a short time, and with minimal thermal damage. In the refracting system, the thermal energy of hot water is transferred to the fruit slices placed on the film through a polymer film. The moisture removal process in this system is fast, under atmospheric pressure, self-regulating, and at a temperature lower than the temperature of the hot water used, so thermal damage to the material being dried is minimized. The resulting water vapor is removed from the dryer chamber by a fan (Ortez-Jeres, 2015). Researchers have used various methods in the field of drying agricultural products and cut fruits. Given the extensive research conducted on these methods, this study attempts to address the research conducted on the refractivity window dryer system.<br /><em>Materials and Methods</em><br />Thomson variety of oranges were obtained from orchards in the city of Jooybar, Mazandaran province. First, the oranges were washed in water and after drying, they were sliced into 4, 6, and 8 mm thick slices using a slicer. All samples were weighed before drying. The weight of the samples was measured using a digital scale (AND-EK-600G) with an accuracy of ±0.01. A refractance window dryer was used to dry the samples. The refractance window device used in the experiments is located at the Mashhad Agricultural Jihad Research and Development Center, which uses hot water circulation as a heat source with a boiling temperature at atmospheric pressure, which can be changed with the water temperature controller due to the elements inside the tank. Hot water in the hot water bath is circulated through a heating unit to maintain a constant water temperature and increase thermal efficiency. A valve is installed between the hot water bath and the water heating tank to pump water until the water temperature is lower than the desired value. A pump is provided to pump hot water from the hot water tank to the bath. The heat energy from heating the water is transferred to the product through a Mylar polyester plastic sheet. For drying, the predetermined samples are spread on a transparent plastic sheet (Mylar) and its bottom is placed in contact with hot water from a shallow container. The Mylar sheets pass the pure energy from the hot water through conduction and radiation, causing the product to dry. Also, two fans are located at the top of the Mylar sheet to remove the moisture created in the chamber. Orange slices with a thickness of 4 mm were placed in a single layer on the drying chamber tray, and experimental treatments for drying thin slices of apple were performed, including drying temperatures from 30 to 70 degrees Celsius, with an increase of 10 degrees Celsius, and air speeds from 1 to 2 meters per second, with an increase of 0.5 meters per second. After turning on the heat pump dryer, the temperature and air speed were adjusted to the desired treatment, and then the drying process began. Drying continued until the weight of the thin slices of apple was approximately constant, and the collected data were recorded every ten minutes. A factorial experimental design based on a completely randomized design with three replications was used for data analysis.<br /><em>Results and Discussion</em><br />The drying intensity decreased continuously with drying time. As can be seen, increasing the drying temperature resulted in an increase in the drying rate and consequently a decrease in the drying time. With increasing drying temperature and due to the intensification of the heat transfer rate, water molecules moved faster and accelerated the water transfer from the product. The higher drying rate occurred during the initial drying period, which was due to lower external resistance and greater water migration into the product. As the thickness of the samples decreased, the time to reach the end point decreased from 190 minutes for 8 mm thickness to 140 minutes for 4 mm thickness. Also, the moisture removal rate was higher in the initial stage of drying, which then decreased. It is obvious that the time to reach the end point of drying of orange slices changes with the thickness of the sample. This indicates that the thickness of the sample affects the drying time. It can be seen that among these equations, the three mathematical models of modified Page, logarithmic and Medley et al. with a coefficient of determination higher than 0.99 can well describe the law of moisture change. Among them, the modified Page model has the highest R<sup>2</sup> and the lowest χ<sup>2</sup> and RMSE. It can be concluded that the modified Page model is the best model for describing the drying of sliced orange slices in a refractance window dryer. It was found that its range of variation changed from 39.6× 10<sup>-10</sup> to 42.5× 10<sup>-10</sup> m<sup>2</sup>/s. As expected, the D<sub>eff</sub> values increased with increasing air temperature during the drying process, which was due to the increase in vapor pressure inside the samples, which led to molecular motion and rapid movement of water at high temperatures. Also, with increasing thickness, the mass transfer rate increased, which led to an increase in the effective diffusion coefficient. The highest and lowest activation energies were 28.38 and 21.94 kJ/mol, respectively, for treatments at 90°C and 8 mm thickness and 60°C and 4 mm thickness.<br /><em>Conclusion</em><br />The use of refractivity window for drying orange slices was tested and the best model was presented using mathematical models to fit the experimental data and predict the drying behavior. Increasing the temperature and decreasing the thickness of the orange slices both accelerate the drying process. Among them, the modified Page model has the highest R<sup>2 </sup>and the lowest χ<sup>2</sup> and RMSE. It can be concluded that the modified Page model is the best model to describe the drying of orange slices in the refractivity window dryer. The D<sub>eff</sub> values increased with increasing drying temperature, which is due to the increase in vapor pressure inside the samples, which leads to rapid movement of water at high temperatures. The activation energy for moisture diffusion was obtained from the Arrhenius equation as 27.5 kJ/mol.In this study, the drying parameters and kinetics of sliced orange (Thomson variety) were investigated in a refractance window dryer at temperatures (60, 75, and 90 degrees Celsius) and slice thicknesses (4, 6, and 8 mm). Analysis of variance was used to determine the effects of temperature and thickness on the parameters of color change, shrinkage, and resorption intensity. Five mathematical models were selected to describe and compare the drying kinetics of orange slices, and the coefficient of determination (R<sup>2</sup>), chi-square (χ<sup>2</sup>), and root mean square error (RMSE) were used for evaluation. Also, the moisture transfer from orange slices was described by fitting the Fick diffusion model. The results showed that drying temperature and slice thickness had a significant effect on the drying behavior of orange slices. Drying time decreased with increasing temperature and decreasing thickness. Temperature and thickness had little effect on the total color changes, shrinkage rate, and resorption intensity of dried orange slices. Among the mathematical models, the modified Page model had the best fit with the lowest error and the highest coefficient of determination. The effective diffusion coefficient (D<sub>eff</sub>) increased with increasing drying temperature and was found to be in the range of 39.6× 10<sup>-10</sup> - 42.5× 10<sup>-10</sup> m<sup>2</sup>/s. The temperature dependence of the effective diffusion was described by the Arrhenius equation and the activation energy for moisture diffusion in orange slices was determined to be 27.5 kJ/mol..<br /><em>Introduction</em><br />Iran ranks seventh in the world in terms of citrus production. The country's annual citrus production is about six million tons, of which three million tons are oranges. A significant portion of the oranges produced, after consumption as fresh fruit and export, can be used in various processing and complementary industries, including fruit juice and dried fruit. Since fruit drying can be done on a small and home scale, the development of suitable dryers with high energy efficiency is very important. Refractance Window, known as the fourth generation of dryers, is a moisture removal system for producing high-quality dried or concentrated foods. Refractance window dryers can produce dried products with relatively low energy consumption, in a short time, and with minimal thermal damage. In the refracting system, the thermal energy of hot water is transferred to the fruit slices placed on the film through a polymer film. The moisture removal process in this system is fast, under atmospheric pressure, self-regulating, and at a temperature lower than the temperature of the hot water used, so thermal damage to the material being dried is minimized. The resulting water vapor is removed from the dryer chamber by a fan (Ortez-Jeres, 2015). Researchers have used various methods in the field of drying agricultural products and cut fruits. Given the extensive research conducted on these methods, this study attempts to address the research conducted on the refractivity window dryer system.<br /><em>Materials and Methods</em><br />Thomson variety of oranges were obtained from orchards in the city of Jooybar, Mazandaran province. First, the oranges were washed in water and after drying, they were sliced into 4, 6, and 8 mm thick slices using a slicer. All samples were weighed before drying. The weight of the samples was measured using a digital scale (AND-EK-600G) with an accuracy of ±0.01. A refractance window dryer was used to dry the samples. The refractance window device used in the experiments is located at the Mashhad Agricultural Jihad Research and Development Center, which uses hot water circulation as a heat source with a boiling temperature at atmospheric pressure, which can be changed with the water temperature controller due to the elements inside the tank. Hot water in the hot water bath is circulated through a heating unit to maintain a constant water temperature and increase thermal efficiency. A valve is installed between the hot water bath and the water heating tank to pump water until the water temperature is lower than the desired value. A pump is provided to pump hot water from the hot water tank to the bath. The heat energy from heating the water is transferred to the product through a Mylar polyester plastic sheet. For drying, the predetermined samples are spread on a transparent plastic sheet (Mylar) and its bottom is placed in contact with hot water from a shallow container. The Mylar sheets pass the pure energy from the hot water through conduction and radiation, causing the product to dry. Also, two fans are located at the top of the Mylar sheet to remove the moisture created in the chamber. Orange slices with a thickness of 4 mm were placed in a single layer on the drying chamber tray, and experimental treatments for drying thin slices of apple were performed, including drying temperatures from 30 to 70 degrees Celsius, with an increase of 10 degrees Celsius, and air speeds from 1 to 2 meters per second, with an increase of 0.5 meters per second. After turning on the heat pump dryer, the temperature and air speed were adjusted to the desired treatment, and then the drying process began. Drying continued until the weight of the thin slices of apple was approximately constant, and the collected data were recorded every ten minutes. A factorial experimental design based on a completely randomized design with three replications was used for data analysis.<br /><em>Results and Discussion</em><br />The drying intensity decreased continuously with drying time. As can be seen, increasing the drying temperature resulted in an increase in the drying rate and consequently a decrease in the drying time. With increasing drying temperature and due to the intensification of the heat transfer rate, water molecules moved faster and accelerated the water transfer from the product. The higher drying rate occurred during the initial drying period, which was due to lower external resistance and greater water migration into the product. As the thickness of the samples decreased, the time to reach the end point decreased from 190 minutes for 8 mm thickness to 140 minutes for 4 mm thickness. Also, the moisture removal rate was higher in the initial stage of drying, which then decreased. It is obvious that the time to reach the end point of drying of orange slices changes with the thickness of the sample. This indicates that the thickness of the sample affects the drying time. It can be seen that among these equations, the three mathematical models of modified Page, logarithmic and Medley et al. with a coefficient of determination higher than 0.99 can well describe the law of moisture change. Among them, the modified Page model has the highest R<sup>2</sup> and the lowest χ<sup>2</sup> and RMSE. It can be concluded that the modified Page model is the best model for describing the drying of sliced orange slices in a refractance window dryer. It was found that its range of variation changed from 39.6× 10<sup>-10</sup> to 42.5× 10<sup>-10</sup> m<sup>2</sup>/s. As expected, the D<sub>eff</sub> values increased with increasing air temperature during the drying process, which was due to the increase in vapor pressure inside the samples, which led to molecular motion and rapid movement of water at high temperatures. Also, with increasing thickness, the mass transfer rate increased, which led to an increase in the effective diffusion coefficient. The highest and lowest activation energies were 28.38 and 21.94 kJ/mol, respectively, for treatments at 90°C and 8 mm thickness and 60°C and 4 mm thickness.<br /><em>Conclusion</em><br />The use of refractivity window for drying orange slices was tested and the best model was presented using mathematical models to fit the experimental data and predict the drying behavior. Increasing the temperature and decreasing the thickness of the orange slices both accelerate the drying process. Among them, the modified Page model has the highest R<sup>2 </sup>and the lowest χ<sup>2</sup> and RMSE. It can be concluded that the modified Page model is the best model to describe the drying of orange slices in the refractivity window dryer. The D<sub>eff</sub> values increased with increasing drying temperature, which is due to the increase in vapor pressure inside the samples, which leads to rapid movement of water at high temperatures. The activation energy for moisture diffusion was obtained from the Arrhenius equation as 27.5 kJ/mol.https://jam.tabrizu.ac.ir/article_20343_82e9ab8b9d1767efb6efa89d4ab8a811.pdfUniversity of TabrizJournal of Agricultural Mechanization2383-126X10320251222Development and Evaluation of an Internet of Things-based Smart System for Apple Codling Moth Forecasting in the Orchard Using a Wireless Sensor NetworkDevelopment and Evaluation of an Internet of Things-based Smart System for Apple Codling Moth Forecasting in the Orchard Using a Wireless Sensor Network17302050010.22034/jam.2025.68086.1333FABaharehJamshidiSmart Agricultural Research department, Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.KazemMohammadpourAgricultural Entomology Research Department, Iranian Research Institute of Plant Protection, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.HosseinFarazmandAgricultural Entomology Research Department, Iranian Research Institute of Plant Protection, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.Journal Article20250711<em>Introduction</em><br />The apple codling moth, Cydia pomonella (Lep.: Tortricidae), is one of the key pests of the apple trees. This pest attacks apple trees in significant numbers every year, causing damage that always exceeds the level of economic loss. Therefore, apple codling moth forecasting is very important to determine the most appropriate time for spraying to control the pest, which plays a major role in apple orchard management. Using modern and smart methods for pest forecasting is very important to succeed in chemical control and reducing the number of spraying times in the orchard. Internet of Things (IoT) technology and its smart solutions, based on the basic principles of sensor networks, can enable smart environments and data-based decision-making. In this research, a smart system based on the Internet of Things and a Wireless Sensor Network (WSN) technologies was developed for forecasting of the apple codling moth in the orchard. The effectiveness of using the designed system was also investigated in an apple orchard located in Tehran province (Damavand County).<br /> <br /><em>Materials and Methods</em><br />A wireless sensor node was used to collect on-line data of the ambient temperature in the orchard. The data was transported to the gateway through LoRa radio protocol, a long-range and low-power protocol for the Internet of Things. They were sent from the gateway to the network server and then made available to the software designed for the system. The main heart of the software for decision-making was the apple codling moth forecasting model, which was determined based on the hour-degree Celsius. For this purpose, the biofix of apple codling moth pest was determined using pheromone traps and the ambient temperature was recorded hourly using the wireless sensor node installed in the orchard to calculate the total effective environmental temperature. Based on the temperature data and using a phenological forecasting model, the most appropriate spraying time for controlling the apple codling moth was determined and included in the designed software of the system. A dashboard was also designed to display the results. The efficiency of the designed smart system and its forecasting model for controlling apple codling moth pest in the orchard was evaluated over two years.<br /> <br /><em>Results and Discussion</em><br />The Internet of Things-based smart system designed for apple codling moth forecasting pest in the orchard could announce the appropriate time for spraying, along with the type and dosage of the pesticide. This smart system has excellent reliability in data transmission with zero data loss and has excellent accuracy (100%) in terms of timely warning announcements. Evaluations showed that the designed system reduces the damage caused by the apple codling moth pest by reducing the number of spraying times from four to two, which will increase the yield and improve the quality of the product. The results of the two-year investigation indicated that the damage caused by the apple codling moth pest at the harvest time in the control trees was more than 70% greater than in trees that were sprayed based on the forecasting model of the designed system. This is even though according to the orchard manager's statements, the average number of spraying times in previous years, based on predictions made with pheromone traps, has been four, and the damage caused by the codling moth has exceeded the amount estimated in this research. Spraying times for controlling the apple codling moth was reduced to twice a year in the orchard by using the smart system developed based on the hour-degree Celsius forecasting model.<br /> <br /><em>Conclusion</em><br /> <br />Given the high efficiency of the smart system designed based on the determined forecasting model in controlling the apple codling moth pest and reducing the use and costs of pesticides by 50%, and consequently a 50% reduction in labor costs and pesticide spraying equipment required for each time of spraying in the orchard, the use of this Internet of Things-based system is recommended for forecasting of this pest in apple orchards. Reducing the risk of contamination for the workers who spray pesticides and reducing the amount of pesticide residue in the product are indirect positive effects of using the designed smart system. Future work in the continuation of this research is to pay attention to making the biofix determination process smarter by developing smart traps that can be connected to the system's Wireless Sensor Network.<br /> <br /><em>Acknowledgment</em><br />We would like to thank Future Wave Ultratech Company for the cooperation and providing the necessary equipment and infrastructure to conduct this research. We are also grateful to the manager of the apple orchard for his cooperation.<em>Introduction</em><br />The apple codling moth, Cydia pomonella (Lep.: Tortricidae), is one of the key pests of the apple trees. This pest attacks apple trees in significant numbers every year, causing damage that always exceeds the level of economic loss. Therefore, apple codling moth forecasting is very important to determine the most appropriate time for spraying to control the pest, which plays a major role in apple orchard management. Using modern and smart methods for pest forecasting is very important to succeed in chemical control and reducing the number of spraying times in the orchard. Internet of Things (IoT) technology and its smart solutions, based on the basic principles of sensor networks, can enable smart environments and data-based decision-making. In this research, a smart system based on the Internet of Things and a Wireless Sensor Network (WSN) technologies was developed for forecasting of the apple codling moth in the orchard. The effectiveness of using the designed system was also investigated in an apple orchard located in Tehran province (Damavand County).<br /> <br /><em>Materials and Methods</em><br />A wireless sensor node was used to collect on-line data of the ambient temperature in the orchard. The data was transported to the gateway through LoRa radio protocol, a long-range and low-power protocol for the Internet of Things. They were sent from the gateway to the network server and then made available to the software designed for the system. The main heart of the software for decision-making was the apple codling moth forecasting model, which was determined based on the hour-degree Celsius. For this purpose, the biofix of apple codling moth pest was determined using pheromone traps and the ambient temperature was recorded hourly using the wireless sensor node installed in the orchard to calculate the total effective environmental temperature. Based on the temperature data and using a phenological forecasting model, the most appropriate spraying time for controlling the apple codling moth was determined and included in the designed software of the system. A dashboard was also designed to display the results. The efficiency of the designed smart system and its forecasting model for controlling apple codling moth pest in the orchard was evaluated over two years.<br /> <br /><em>Results and Discussion</em><br />The Internet of Things-based smart system designed for apple codling moth forecasting pest in the orchard could announce the appropriate time for spraying, along with the type and dosage of the pesticide. This smart system has excellent reliability in data transmission with zero data loss and has excellent accuracy (100%) in terms of timely warning announcements. Evaluations showed that the designed system reduces the damage caused by the apple codling moth pest by reducing the number of spraying times from four to two, which will increase the yield and improve the quality of the product. The results of the two-year investigation indicated that the damage caused by the apple codling moth pest at the harvest time in the control trees was more than 70% greater than in trees that were sprayed based on the forecasting model of the designed system. This is even though according to the orchard manager's statements, the average number of spraying times in previous years, based on predictions made with pheromone traps, has been four, and the damage caused by the codling moth has exceeded the amount estimated in this research. Spraying times for controlling the apple codling moth was reduced to twice a year in the orchard by using the smart system developed based on the hour-degree Celsius forecasting model.<br /> <br /><em>Conclusion</em><br /> <br />Given the high efficiency of the smart system designed based on the determined forecasting model in controlling the apple codling moth pest and reducing the use and costs of pesticides by 50%, and consequently a 50% reduction in labor costs and pesticide spraying equipment required for each time of spraying in the orchard, the use of this Internet of Things-based system is recommended for forecasting of this pest in apple orchards. Reducing the risk of contamination for the workers who spray pesticides and reducing the amount of pesticide residue in the product are indirect positive effects of using the designed smart system. Future work in the continuation of this research is to pay attention to making the biofix determination process smarter by developing smart traps that can be connected to the system's Wireless Sensor Network.<br /> <br /><em>Acknowledgment</em><br />We would like to thank Future Wave Ultratech Company for the cooperation and providing the necessary equipment and infrastructure to conduct this research. We are also grateful to the manager of the apple orchard for his cooperation.https://jam.tabrizu.ac.ir/article_20500_1374063a26d0be81136be6a21a1a9fb7.pdfUniversity of TabrizJournal of Agricultural Mechanization2383-126X10320251222Intelligent Monitoring of Broilers Behavior in Poultry Farms Using Deep LearningIntelligent Monitoring of Broilers Behavior in Poultry Farms Using Deep Learning31422050110.22034/jam.2025.67931.1329FAHosseinAkhtariDepartment of Biosystem Engineering, Faculty of Agriculture, University of Tabriz, Tabriz. IranHosseinNavidDepartment of Biosystem Engineering, Faculty of Agriculture, University of Tabriz, Tabriz. IranAbasaltBazrafshanDepartment of Biosystem Engineering, Faculty of Agriculture, University of Tabriz, Tabriz. IranMajidOlyayeeDepartment of Animal Science, Faculty of Agriculture, University of Tabriz. IranAliGhaffarnezhadDepartment of Biosystem Engineering, Faculty of Agriculture, University of Tabriz, Tabriz. IranJournal Article20250629<em>Introduction</em><br />Poultry production serves as a crucial sector in the global food supply chain by offering a cost effective and protein rich source of nutrition to meet the demands of a rapidly growing population. As global consumption of poultry meat continues to increase, ensuring the health and welfare of broiler chickens has become more essential than ever. One key indicator of flock health and environmental quality in broiler production systems is animal behavior. Monitoring behaviors such as feeding, drinking, and general activity can provide valuable insights into welfare status, environmental conditions, and management efficiency. However, conventional methods for behavior monitoring typically rely on manual observation, which is labor-intensive, inconsistent, and impractical for large scale or continuous surveillance in commercial settings.<br />Advancements in computer vision and deep learning have opened new possibilities for automating behavior analysis in livestock farming. In particular, object detection models based on convolutional neural networks (CNNs) have shown high accuracy in detecting animals and recognizing specific postures or activities. This study explores the application of YOLOv11s, a recent lightweight yet powerful object detection model, to recognize and classify broiler behaviors from top view images captured in a real poultry farm environment. The primary aim is to develop an efficient and real-time monitoring system that can automatically detect key behaviors, reduce human intervention, and ultimately support precision poultry farming with enhanced animal welfare management.<br /> <br /><em>Materials and Methods</em><br />This study was conducted at the research poultry farm of the university of Tabriz. Images of broiler chickens were captured from 48 separate pens to monitor their spatial distribution and behaviors. The top view imaging setup helped minimize occlusions and provided a clear view of the broilers' positions and actions. The collected images were annotated manually by experts into three behavioral categories: normal, feeding, and drinking. Preprocessing steps were applied to the raw images to enhance model training. These included resizing, normalization, and data augmentation techniques such as horizontal flipping and brightness adjustment. The dataset was divided into training, validation, and testing subsets, considering class imbalance and ensuring representative distribution of behaviors. Specific attention was given to improving the annotation quality to support accurate model learning.<br />For behavior detection, the YOLOv11s model a lightweight version of the YOLOv11 object detection architecture was employed due to its high inference speed and accuracy. The model was customized and trained using fine-tuned hyperparameters to achieve optimal performance. The training process was monitored using metrics such as loss convergence and mean Average Precision (mAP) at IoU 0.5. This setup enabled effective and real-time detection of broiler behaviors in smart poultry farming systems.<br /> <br /><em>Results and Discussion</em><br />The YOLOv11s model was successfully trained to detect and classify three distinct broiler behaviors: feeding, drinking, and normal activity, based on top view images collected from 48 pens in a research oriented poultry facility. The model achieved a mean Average Precision (mAP) of 90% at IoU threshold 0.5, demonstrating high overall accuracy in behavior recognition. It performed particularly well in detecting feeding behavior (99% precision) and drinking behavior (89% precision), while performance for normal behavior was slightly lower (82%) due to the underrepresentation of this class during annotation. Although mild overfitting was observed during training, this issue was alleviated through regularization techniques such as dropout and early stopping, allowing the model to generalize well to unseen data.<br />Spatial analysis of detections revealed clear clustering of feeding and drinking behaviors around feeders and drinkers, respectively, while instances of normal activity were more evenly distributed across the pen space. This distribution pattern not only validates the behavioral labels but also suggests that the system can be used to assess flock dynamics and detect anomalies in movement or engagement. Furthermore, the lightweight architecture of YOLOv11s enabled real-time inference with minimal computational overhead, making it a practical solution for continuous on farm monitoring. These results are consistent with trends reported in prior studies using deep learning for animal behavior detection, while offering a novel, efficient, and scalable approach tailored for precision poultry farming.<br /> <br /><em>Conclusion</em><br />This study demonstrated the effectiveness of the YOLOv11s deep learning model for automated recognition of broiler behaviors from top view images. The model accurately classified three key behaviors: feeding, drinking, and normal activity, using lightweight processing suitable for real-time monitoring. High detection accuracy, particularly for feeding (99%) and drinking (89%), along with acceptable performance for normal behavior (82%), confirmed the reliability of the proposed system in practical scenarios. Spatial patterns of the behaviors also aligned with expected distributions, reinforcing the validity of the detection results. The findings suggest that integrating YOLOv11s into smart poultry farming systems can enhance real-time flock observation, reduce human labor, and improve management decision making. The lightweight nature of the model makes it suitable for edge deployment, enabling cost effective and scalable solutions for precision livestock monitoring. Overall, this research highlights the feasibility of using deep learning for intelligent broiler behavior analysis, contributing to better welfare, health assessment, and efficient poultry production.<br /> <br /><em>Acknowledgement</em><br />The authors would like to express their sincere appreciation to the staff of the KhalatPoushan Research Station at the University of Tabriz and the students of the poultry research facility for providing support and resources during the data collection process.<em>Introduction</em><br />Poultry production serves as a crucial sector in the global food supply chain by offering a cost effective and protein rich source of nutrition to meet the demands of a rapidly growing population. As global consumption of poultry meat continues to increase, ensuring the health and welfare of broiler chickens has become more essential than ever. One key indicator of flock health and environmental quality in broiler production systems is animal behavior. Monitoring behaviors such as feeding, drinking, and general activity can provide valuable insights into welfare status, environmental conditions, and management efficiency. However, conventional methods for behavior monitoring typically rely on manual observation, which is labor-intensive, inconsistent, and impractical for large scale or continuous surveillance in commercial settings.<br />Advancements in computer vision and deep learning have opened new possibilities for automating behavior analysis in livestock farming. In particular, object detection models based on convolutional neural networks (CNNs) have shown high accuracy in detecting animals and recognizing specific postures or activities. This study explores the application of YOLOv11s, a recent lightweight yet powerful object detection model, to recognize and classify broiler behaviors from top view images captured in a real poultry farm environment. The primary aim is to develop an efficient and real-time monitoring system that can automatically detect key behaviors, reduce human intervention, and ultimately support precision poultry farming with enhanced animal welfare management.<br /> <br /><em>Materials and Methods</em><br />This study was conducted at the research poultry farm of the university of Tabriz. Images of broiler chickens were captured from 48 separate pens to monitor their spatial distribution and behaviors. The top view imaging setup helped minimize occlusions and provided a clear view of the broilers' positions and actions. The collected images were annotated manually by experts into three behavioral categories: normal, feeding, and drinking. Preprocessing steps were applied to the raw images to enhance model training. These included resizing, normalization, and data augmentation techniques such as horizontal flipping and brightness adjustment. The dataset was divided into training, validation, and testing subsets, considering class imbalance and ensuring representative distribution of behaviors. Specific attention was given to improving the annotation quality to support accurate model learning.<br />For behavior detection, the YOLOv11s model a lightweight version of the YOLOv11 object detection architecture was employed due to its high inference speed and accuracy. The model was customized and trained using fine-tuned hyperparameters to achieve optimal performance. The training process was monitored using metrics such as loss convergence and mean Average Precision (mAP) at IoU 0.5. This setup enabled effective and real-time detection of broiler behaviors in smart poultry farming systems.<br /> <br /><em>Results and Discussion</em><br />The YOLOv11s model was successfully trained to detect and classify three distinct broiler behaviors: feeding, drinking, and normal activity, based on top view images collected from 48 pens in a research oriented poultry facility. The model achieved a mean Average Precision (mAP) of 90% at IoU threshold 0.5, demonstrating high overall accuracy in behavior recognition. It performed particularly well in detecting feeding behavior (99% precision) and drinking behavior (89% precision), while performance for normal behavior was slightly lower (82%) due to the underrepresentation of this class during annotation. Although mild overfitting was observed during training, this issue was alleviated through regularization techniques such as dropout and early stopping, allowing the model to generalize well to unseen data.<br />Spatial analysis of detections revealed clear clustering of feeding and drinking behaviors around feeders and drinkers, respectively, while instances of normal activity were more evenly distributed across the pen space. This distribution pattern not only validates the behavioral labels but also suggests that the system can be used to assess flock dynamics and detect anomalies in movement or engagement. Furthermore, the lightweight architecture of YOLOv11s enabled real-time inference with minimal computational overhead, making it a practical solution for continuous on farm monitoring. These results are consistent with trends reported in prior studies using deep learning for animal behavior detection, while offering a novel, efficient, and scalable approach tailored for precision poultry farming.<br /> <br /><em>Conclusion</em><br />This study demonstrated the effectiveness of the YOLOv11s deep learning model for automated recognition of broiler behaviors from top view images. The model accurately classified three key behaviors: feeding, drinking, and normal activity, using lightweight processing suitable for real-time monitoring. High detection accuracy, particularly for feeding (99%) and drinking (89%), along with acceptable performance for normal behavior (82%), confirmed the reliability of the proposed system in practical scenarios. Spatial patterns of the behaviors also aligned with expected distributions, reinforcing the validity of the detection results. The findings suggest that integrating YOLOv11s into smart poultry farming systems can enhance real-time flock observation, reduce human labor, and improve management decision making. The lightweight nature of the model makes it suitable for edge deployment, enabling cost effective and scalable solutions for precision livestock monitoring. Overall, this research highlights the feasibility of using deep learning for intelligent broiler behavior analysis, contributing to better welfare, health assessment, and efficient poultry production.<br /> <br /><em>Acknowledgement</em><br />The authors would like to express their sincere appreciation to the staff of the KhalatPoushan Research Station at the University of Tabriz and the students of the poultry research facility for providing support and resources during the data collection process.https://jam.tabrizu.ac.ir/article_20501_edbfc632c9806941e1b764e549d786c1.pdfUniversity of TabrizJournal of Agricultural Mechanization2383-126X10320251222Application of Machine Vision Method in Diagnosing Strawberry Diseases with YOLO 11Application of Machine Vision Method in Diagnosing Strawberry Diseases with YOLO 1143562052110.22034/jam.2025.67883.1328FANashmilFarhadiDepartment of Mechanics Engineering of Biosystems, Faculty of Agriculture, Urmia University, Urmia, IranRahmanFarrokhi TeimourlouDepartment of Mechanics Engineering of Biosystems, Faculty of Agriculture, Urmia University, Urmia, IranJournal Article20250627<em>Introduction</em><br />Strawberries are one of the most important and valuable garden crops, widely supplied to global agro-markets, as well as food and pharmaceutical industries, due to their high nutritional and economic value, antioxidant compounds, and favorable taste. However, strawberry production is often accompanied by challenges, one of the most important of which is prevalence of plant diseases. Diseases of fungal or bacterial origin typically cause damage to the crop growth, which may reduce the yield, and in some cases, lead to the complete harvest failure and significant financial losses. Timely and accurate identification of these diseases plays a crucial role in effective farm management. Traditional methods, such as visual inspection, require considerable expertise, are time-consuming, prone to errors, and often yield suboptimal results. In recent years, advancements in technologies related to artificial intelligence and machine learning, particularly in machine vision models, have made it possible to automatically identify plant diseases with higher speed and accuracy. In this study, the YOLO algorithm, one of the most widely used and advanced methods in digital object recognition, was employed to identify various strawberry diseases, including angular leaf spots, anthracnose, gray mold, and powdery mildew. To improve the accuracy of the model, modifications were made to the network architecture and training process. In addition to high accuracy and appropriate speed, this method may reduce the costs related to monitoring and managing strawberry farms. The results obtained from this study demonstrate that the YOLO algorithm can be effectively utilized in smart agriculture, in conjunction with specific equipment and tools such as drones and image sensors, to control diseases and enhance production. This research represents a practical step toward utilizing modern technologies to manage plant diseases and enhance agricultural productivity.<br /> <br /><em>Materials and Methods</em><br />The dataset used consisted of 2902 images of strawberry leaves and fruits collected from the Roboflow database. The whole dataset was divided into three distinct portions: training (70%), validation (20%), and test (10%). All images were set to 640×640 pixels, and the labeling process was performed according to YOLO standards. Eight disease classes, including angular leaf spot, anthracnose rot, blossom blight, gray mold, healthy leaves and fruits, leaf spot, powdery mildew on fruits, and powdery mildew on leaves, were included in the dataset. The YOLO11-Large (YOLO11L) model was trained using pre-trained weights and an object detection task. The Optuna algorithm was used to optimize the hyperparameters. The training process consisted of 200 epochs, utilizing an early stopping mechanism to prevent overfitting. The training batch size was set to 4, and other settings, such as data augmentation, including random rotation, scaling, horizontal and vertical inversion, random cropping, and image blending, were also applied. Finally, a deep learning model based on YOLO11L was used to identify and classify strawberry plant diseases. The final model is consists of 190 layers and approximately 790,000 trainable parameters, which are distributed among three main parts of the network: about 480,000 parameters in the backbone (feature extraction), 285,000 in the neck (feature aggregation), and 25,000 in the head (detection output). The model’s total computational complexity is approximately 6.86 GFLOPs. The processing speed of the model was measured to be 0.5 ms for preprocessing, 23.6 ms for inference, and 2.3 ms for postprocessing per image.<br /> <br /><em>Results and Discussion</em><br />The results show that the evaluation accuracy of the model is the best in case of blossom blight class, where a precision of 0.951 and a full recall of 1.000 were obtained. This indicates the ability of the model to identify this disease without any omission errors. Additionally, the mAP@50 and mAP@50-95 values for this class are 0.995 and 0.882, respectively, which confirm the model's accuracy at all Intersection over union (IoU) thresholds. The angular leaf spot class also demonstrated good performance, falling just short of balance between precision (0.905) and recall (0.904). Additionally, the mAP@50 and mAP@50-95 values for this class are 0.927 and 0.760, respectively, indicating the practical identification of this disease at various levels of overlap. The leaf spot class with the highest number of samples (223) also exhibits strong performance, with a precision of 0.907, recall of 0.914, and mAP@50 of 0.943, confirming that the model has experienced improved learning and generalization with increasing data volume. On the other hand, some classes, such as anthracnose and gray mold, suffer from an imbalance in precision and recall. In the anthracnose class, the high precision (0.952) indicates the ability of the model to avoid type I errors, but the lower recall (0.800) indicates the possibility of undetected samples. Similarly, in the gray mold class, the precision is 0.897, and the recall is 0.812, indicating some challenges in extracting the unique features of this disease. The lower value of mAP@50-95 in this class (0.628) indicates that the model suffers from performance degradation at different levels of spatial accuracy. In the healthy class, the model has a perfect recall (0.942), indicating that almost all healthy samples are correctly identified. However, the lower precision in this class (0.799) suggests that some diseased samples are falsely diagnosed as healthy, which can be risky in real applications, especially in prevention processes. Finally, the fruit and leaf powdery mildew classes have the weakest performance among all available classes. The precision of 0.816 and recall of 0.725 for the fruit powdery mildew class indicate a serious challenge for the model in accurately diagnosing this disease. In particular, the mAP@50-95 value of 0.689 also highlights that the model lacks the necessary stability across different detection scales. Possible reasons for this poor performance may include the lack of data in the relevant classes, the apparent similarity with other classes, e.g., leaf powdery mildew and leaf spot, and the insufficient visual diversity in the dataset.<br /> <br /><em>Conclusion</em><br /><br /><br /><br /><br />This study led to the development of an advanced strawberry disease detection system based on an updated YOLO11L architecture, which achieved an average precision of 90.9% in the mAP@50 benchmark. The proposed model performed very well in identifying diseases with distinct visual symptoms, such as blossom blight (99.5% precision) and leaf spot (94.3% precision). However, a relative decrease in accuracy was observed when classifying diseases with similar visual symptoms, such as fruit powdery mildew (82.7% precision) and gray mold (89.1% precision). This was mainly due to two key factors: (1) insufficient training data for recent, classes and (2) high overlap in visual patterns between them. From an applied perspective, the presented model has significant potential in improving plant disease management solutions, through applications such as intelligent monitoring of farms and greenhouses, integration with unmanned aerial systems for large-scale surveillance, and reducing untargeted pesticide use through accurate and situational disease detection.<em>Introduction</em><br />Strawberries are one of the most important and valuable garden crops, widely supplied to global agro-markets, as well as food and pharmaceutical industries, due to their high nutritional and economic value, antioxidant compounds, and favorable taste. However, strawberry production is often accompanied by challenges, one of the most important of which is prevalence of plant diseases. Diseases of fungal or bacterial origin typically cause damage to the crop growth, which may reduce the yield, and in some cases, lead to the complete harvest failure and significant financial losses. Timely and accurate identification of these diseases plays a crucial role in effective farm management. Traditional methods, such as visual inspection, require considerable expertise, are time-consuming, prone to errors, and often yield suboptimal results. In recent years, advancements in technologies related to artificial intelligence and machine learning, particularly in machine vision models, have made it possible to automatically identify plant diseases with higher speed and accuracy. In this study, the YOLO algorithm, one of the most widely used and advanced methods in digital object recognition, was employed to identify various strawberry diseases, including angular leaf spots, anthracnose, gray mold, and powdery mildew. To improve the accuracy of the model, modifications were made to the network architecture and training process. In addition to high accuracy and appropriate speed, this method may reduce the costs related to monitoring and managing strawberry farms. The results obtained from this study demonstrate that the YOLO algorithm can be effectively utilized in smart agriculture, in conjunction with specific equipment and tools such as drones and image sensors, to control diseases and enhance production. This research represents a practical step toward utilizing modern technologies to manage plant diseases and enhance agricultural productivity.<br /> <br /><em>Materials and Methods</em><br />The dataset used consisted of 2902 images of strawberry leaves and fruits collected from the Roboflow database. The whole dataset was divided into three distinct portions: training (70%), validation (20%), and test (10%). All images were set to 640×640 pixels, and the labeling process was performed according to YOLO standards. Eight disease classes, including angular leaf spot, anthracnose rot, blossom blight, gray mold, healthy leaves and fruits, leaf spot, powdery mildew on fruits, and powdery mildew on leaves, were included in the dataset. The YOLO11-Large (YOLO11L) model was trained using pre-trained weights and an object detection task. The Optuna algorithm was used to optimize the hyperparameters. The training process consisted of 200 epochs, utilizing an early stopping mechanism to prevent overfitting. The training batch size was set to 4, and other settings, such as data augmentation, including random rotation, scaling, horizontal and vertical inversion, random cropping, and image blending, were also applied. Finally, a deep learning model based on YOLO11L was used to identify and classify strawberry plant diseases. The final model is consists of 190 layers and approximately 790,000 trainable parameters, which are distributed among three main parts of the network: about 480,000 parameters in the backbone (feature extraction), 285,000 in the neck (feature aggregation), and 25,000 in the head (detection output). The model’s total computational complexity is approximately 6.86 GFLOPs. The processing speed of the model was measured to be 0.5 ms for preprocessing, 23.6 ms for inference, and 2.3 ms for postprocessing per image.<br /> <br /><em>Results and Discussion</em><br />The results show that the evaluation accuracy of the model is the best in case of blossom blight class, where a precision of 0.951 and a full recall of 1.000 were obtained. This indicates the ability of the model to identify this disease without any omission errors. Additionally, the mAP@50 and mAP@50-95 values for this class are 0.995 and 0.882, respectively, which confirm the model's accuracy at all Intersection over union (IoU) thresholds. The angular leaf spot class also demonstrated good performance, falling just short of balance between precision (0.905) and recall (0.904). Additionally, the mAP@50 and mAP@50-95 values for this class are 0.927 and 0.760, respectively, indicating the practical identification of this disease at various levels of overlap. The leaf spot class with the highest number of samples (223) also exhibits strong performance, with a precision of 0.907, recall of 0.914, and mAP@50 of 0.943, confirming that the model has experienced improved learning and generalization with increasing data volume. On the other hand, some classes, such as anthracnose and gray mold, suffer from an imbalance in precision and recall. In the anthracnose class, the high precision (0.952) indicates the ability of the model to avoid type I errors, but the lower recall (0.800) indicates the possibility of undetected samples. Similarly, in the gray mold class, the precision is 0.897, and the recall is 0.812, indicating some challenges in extracting the unique features of this disease. The lower value of mAP@50-95 in this class (0.628) indicates that the model suffers from performance degradation at different levels of spatial accuracy. In the healthy class, the model has a perfect recall (0.942), indicating that almost all healthy samples are correctly identified. However, the lower precision in this class (0.799) suggests that some diseased samples are falsely diagnosed as healthy, which can be risky in real applications, especially in prevention processes. Finally, the fruit and leaf powdery mildew classes have the weakest performance among all available classes. The precision of 0.816 and recall of 0.725 for the fruit powdery mildew class indicate a serious challenge for the model in accurately diagnosing this disease. In particular, the mAP@50-95 value of 0.689 also highlights that the model lacks the necessary stability across different detection scales. Possible reasons for this poor performance may include the lack of data in the relevant classes, the apparent similarity with other classes, e.g., leaf powdery mildew and leaf spot, and the insufficient visual diversity in the dataset.<br /> <br /><em>Conclusion</em><br /><br /><br /><br /><br />This study led to the development of an advanced strawberry disease detection system based on an updated YOLO11L architecture, which achieved an average precision of 90.9% in the mAP@50 benchmark. The proposed model performed very well in identifying diseases with distinct visual symptoms, such as blossom blight (99.5% precision) and leaf spot (94.3% precision). However, a relative decrease in accuracy was observed when classifying diseases with similar visual symptoms, such as fruit powdery mildew (82.7% precision) and gray mold (89.1% precision). This was mainly due to two key factors: (1) insufficient training data for recent, classes and (2) high overlap in visual patterns between them. From an applied perspective, the presented model has significant potential in improving plant disease management solutions, through applications such as intelligent monitoring of farms and greenhouses, integration with unmanned aerial systems for large-scale surveillance, and reducing untargeted pesticide use through accurate and situational disease detection.https://jam.tabrizu.ac.ir/article_20521_95666088afbe6c23ab80530ec57296aa.pdfUniversity of TabrizJournal of Agricultural Mechanization2383-126X10320251222Technical and Economic Evaluation of Triticale Cultivation Methods as a Second Crop in paddy fields of Guilan ProvinceTechnical and Economic Evaluation of Triticale Cultivation Methods as a Second Crop in paddy fields of Guilan Province57682062410.22034/jam.2025.68912.1340FARoohollahYousefiAssistant Professor, Rice Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Rasht, Iran.Journal Article20250901Introduction<br />Guilan Province is a cornerstone of Iran's rice production, benefitting from a unique and favorable climate. However, rising population pressures and limited resources necessitate optimized land use strategies. Cultivating a second crop following rice harvest presents a critical opportunity to enhance productivity, boost farmer incomes, and promote ecological sustainability. Despite this potential, a sharp and concerning decline in the cultivation area of forage crops—both nationally and within Guilan specifically—underscores the urgency of addressing this issue. Triticale emerges as a promising second crop candidate due to its high yield potential and excellent nutritional value, with prior studies confirming its profitability compared to other cereals. While research on forage crop profitability exists, the development of second cropping in Guilan faces distinct challenges, including mechanization barriers and high production costs. Consequently, this study was designed to conduct a comprehensive technical and economic evaluation of different triticale planting methods as a second crop in Guilan's paddy fields, aiming to identify the most optimal cultivation practice.<br /> <br />Materials and Methods<br />This study aimed to evaluate the efficacy of different planting methods for triticale cultivated as a second crop in the paddy fields of Guilan Province, Iran.The two-year field experiment (2020-2022) was conducted at the Rice Research Institute's experimental station (37°10'N, 49°39'E). Five planting techniques were compared in a completely randomized design: (P1) mechanical broadcasting with a fertilizer spreader followed by furrower incorporation; (P2) mechanical broadcasting with a spreader followed by disc incorporation; (P3) manual broadcasting followed by furrower incorporation; (P4) manual broadcasting followed by disc incorporation; and (P5) fully manual seeding. The evaluation encompassed both technical parameters (e.g., forward speed, theoretical and effective field capacity) and economic indices (including net profit, benefit-cost ratio, and return on investment). All collected data were statistically analyzed using SPSS and Excel software.<br /> <br />Results and Discussion<br />This study evaluated various methods for cultivating triticale, assessing their operational efficiency, impact on yield, and economic profitability. The analysis revealed a profound advantage of mechanization over manual labor. Mechanized techniques, especially those using broad-application spreaders, were exceptionally efficient, achieving a field capacity of 1.0 hectare per hour. In stark contrast, manual seeding was drastically slower, requiring 238.46 man-hours per hectare, creating a significant bottleneck that prolonged entire operations. A rigorous statistical analysis confirmed that the cultivation method had a highly significant impact on yield. The method involving a rotavator combined with manual seeding consistently delivered the highest yields for both fresh and dry forage. However, this high-yielding method proved to be the least economical. It incurred the highest production cost, resulting in a loss-making benefit-cost ratio for both fresh and dry forage, making it an unsustainable choice.his economic assessment evaluated various triticale cultivation methods in Guilan Province, considering both fresh and dry forage yields. For fresh forage, Method P5 (manual seeding) incurred the highest production cost at 254,405,000 Rials/hectare, while Method P2 (spreader sowing + disc incorporation) demonstrated the lowest cost per kilogram, amounting to 3,180.9 Rials/kg. Method P2 emerged as the most profitable, yielding a net profit of 25,275,430 Rials/hectare with a favorable benefit-cost ratio (BCR) of 1.38. It was followed by Methods P1 (15,876,070 Rials/hectare, BCR 1.24) and P3 (2,498,310 Rials/hectare, BCR 1.03). Method P2 showed strong financial performance with a sales return rate of 27.71% and a return on investment of 38.33%. Conversely, Methods P5 (BCR 0.47) and P4 (BCR 0.88) were identified as uneconomical due to their BCRs falling below one. In the scenario for dry forage, Method P5 (manual seeding) again presented the highest final production cost at 26,850 Rials/kg, whereas Method P2 (spreader sowing + disc incorporation) was the most cost-efficient at 7,447 Rials/kg. Method P2 significantly outperformed other treatments in profitability, achieving a net profit of 111,153,750 Rials/hectare and a robust BCR of 2.69. Methods P1 (95,795,750 Rials/hectare, BCR 2.46) and P3 (58,447,750 Rials/hectare, BCR 1.81) secured the second and third ranks, respectively. For Method P2, the sales return rate was notably high at 62.76%, with a corresponding return on investment of 168.55%. Method P5 proved uneconomical for dry forage production as well, with a BCR of 0.74. In conclusion, the economic analysis strongly supports Method P2 (spreader sowing + disc seed incorporation) as the optimal and most profitable approach for triticale cultivation in Guilan Province, irrespective of whether the goal is fresh or dry forage. Its superior profitability and excellent investment returns, contrasted with the uneconomical nature of Methods P4 and P5 (particularly for fresh forage, and P5 for dry forage), clearly highlight its advantage.<br /> <br /><em>Conclusion</em><br />According to the findings, from a technical standpoint, the sowing method using a fertilizer spreader followed by seed incorporation with a disc (P2) proved to be the most time-efficient, requiring 15.6 hours per hectare. This method, along with treatment P1 (16.6 hours/hectare), demonstrated a significant advantage in operational speed and efficiency compared to manual broadcasting and seeding methods (P3: 17.3 hours/hectare, P4: 17.17 hours/hectare, and P5: 238.46 hours/hectare).Economically, for fresh forage harvest, method P2 yielded the best results, with a net profit of 25,275,430 Rials per hectare and a benefit-cost ratio (BCR) of 1.38. In contrast, methods P4 (BCR 0.88) and P5 (BCR 0.47) were found to be uneconomical. Similarly, for dry forage harvest, method P2 again led with a net profit of 111,153,750 Rials per hectare and a BCR of 2.69, while P5 (BCR 0.74) lacked economic justification.Therefore, considering operational speed, efficiency, high profitability, and a favorable return on investment for both fresh and dry forage yields, the sowing method using a fertilizer spreader combined with disc seed incorporation (P2) is recommended as the optimal approach for triticale cultivation in Guilan Province.Introduction<br />Guilan Province is a cornerstone of Iran's rice production, benefitting from a unique and favorable climate. However, rising population pressures and limited resources necessitate optimized land use strategies. Cultivating a second crop following rice harvest presents a critical opportunity to enhance productivity, boost farmer incomes, and promote ecological sustainability. Despite this potential, a sharp and concerning decline in the cultivation area of forage crops—both nationally and within Guilan specifically—underscores the urgency of addressing this issue. Triticale emerges as a promising second crop candidate due to its high yield potential and excellent nutritional value, with prior studies confirming its profitability compared to other cereals. While research on forage crop profitability exists, the development of second cropping in Guilan faces distinct challenges, including mechanization barriers and high production costs. Consequently, this study was designed to conduct a comprehensive technical and economic evaluation of different triticale planting methods as a second crop in Guilan's paddy fields, aiming to identify the most optimal cultivation practice.<br /> <br />Materials and Methods<br />This study aimed to evaluate the efficacy of different planting methods for triticale cultivated as a second crop in the paddy fields of Guilan Province, Iran.The two-year field experiment (2020-2022) was conducted at the Rice Research Institute's experimental station (37°10'N, 49°39'E). Five planting techniques were compared in a completely randomized design: (P1) mechanical broadcasting with a fertilizer spreader followed by furrower incorporation; (P2) mechanical broadcasting with a spreader followed by disc incorporation; (P3) manual broadcasting followed by furrower incorporation; (P4) manual broadcasting followed by disc incorporation; and (P5) fully manual seeding. The evaluation encompassed both technical parameters (e.g., forward speed, theoretical and effective field capacity) and economic indices (including net profit, benefit-cost ratio, and return on investment). All collected data were statistically analyzed using SPSS and Excel software.<br /> <br />Results and Discussion<br />This study evaluated various methods for cultivating triticale, assessing their operational efficiency, impact on yield, and economic profitability. The analysis revealed a profound advantage of mechanization over manual labor. Mechanized techniques, especially those using broad-application spreaders, were exceptionally efficient, achieving a field capacity of 1.0 hectare per hour. In stark contrast, manual seeding was drastically slower, requiring 238.46 man-hours per hectare, creating a significant bottleneck that prolonged entire operations. A rigorous statistical analysis confirmed that the cultivation method had a highly significant impact on yield. The method involving a rotavator combined with manual seeding consistently delivered the highest yields for both fresh and dry forage. However, this high-yielding method proved to be the least economical. It incurred the highest production cost, resulting in a loss-making benefit-cost ratio for both fresh and dry forage, making it an unsustainable choice.his economic assessment evaluated various triticale cultivation methods in Guilan Province, considering both fresh and dry forage yields. For fresh forage, Method P5 (manual seeding) incurred the highest production cost at 254,405,000 Rials/hectare, while Method P2 (spreader sowing + disc incorporation) demonstrated the lowest cost per kilogram, amounting to 3,180.9 Rials/kg. Method P2 emerged as the most profitable, yielding a net profit of 25,275,430 Rials/hectare with a favorable benefit-cost ratio (BCR) of 1.38. It was followed by Methods P1 (15,876,070 Rials/hectare, BCR 1.24) and P3 (2,498,310 Rials/hectare, BCR 1.03). Method P2 showed strong financial performance with a sales return rate of 27.71% and a return on investment of 38.33%. Conversely, Methods P5 (BCR 0.47) and P4 (BCR 0.88) were identified as uneconomical due to their BCRs falling below one. In the scenario for dry forage, Method P5 (manual seeding) again presented the highest final production cost at 26,850 Rials/kg, whereas Method P2 (spreader sowing + disc incorporation) was the most cost-efficient at 7,447 Rials/kg. Method P2 significantly outperformed other treatments in profitability, achieving a net profit of 111,153,750 Rials/hectare and a robust BCR of 2.69. Methods P1 (95,795,750 Rials/hectare, BCR 2.46) and P3 (58,447,750 Rials/hectare, BCR 1.81) secured the second and third ranks, respectively. For Method P2, the sales return rate was notably high at 62.76%, with a corresponding return on investment of 168.55%. Method P5 proved uneconomical for dry forage production as well, with a BCR of 0.74. In conclusion, the economic analysis strongly supports Method P2 (spreader sowing + disc seed incorporation) as the optimal and most profitable approach for triticale cultivation in Guilan Province, irrespective of whether the goal is fresh or dry forage. Its superior profitability and excellent investment returns, contrasted with the uneconomical nature of Methods P4 and P5 (particularly for fresh forage, and P5 for dry forage), clearly highlight its advantage.<br /> <br /><em>Conclusion</em><br />According to the findings, from a technical standpoint, the sowing method using a fertilizer spreader followed by seed incorporation with a disc (P2) proved to be the most time-efficient, requiring 15.6 hours per hectare. This method, along with treatment P1 (16.6 hours/hectare), demonstrated a significant advantage in operational speed and efficiency compared to manual broadcasting and seeding methods (P3: 17.3 hours/hectare, P4: 17.17 hours/hectare, and P5: 238.46 hours/hectare).Economically, for fresh forage harvest, method P2 yielded the best results, with a net profit of 25,275,430 Rials per hectare and a benefit-cost ratio (BCR) of 1.38. In contrast, methods P4 (BCR 0.88) and P5 (BCR 0.47) were found to be uneconomical. Similarly, for dry forage harvest, method P2 again led with a net profit of 111,153,750 Rials per hectare and a BCR of 2.69, while P5 (BCR 0.74) lacked economic justification.Therefore, considering operational speed, efficiency, high profitability, and a favorable return on investment for both fresh and dry forage yields, the sowing method using a fertilizer spreader combined with disc seed incorporation (P2) is recommended as the optimal approach for triticale cultivation in Guilan Province.https://jam.tabrizu.ac.ir/article_20624_f7ab2f132be5572483297ed6182192b1.pdfUniversity of TabrizJournal of Agricultural Mechanization2383-126X10320251222Studying the Effect of Coating and Storage Duration on some Quantitative and Qualitative Characteristics of Oranges and Lemons in Above-zero Cold StorageStudying the Effect of Coating and Storage Duration on some Quantitative and Qualitative Characteristics of Oranges and Lemons in Above-zero Cold Storage69832098110.22034/jam.2025.68829.1339FAShabanGhavami JolandanDepartment of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Iran.RezaTabatabaeekoloorAssociate Professor, Department of Biosystems Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, IranJournal Article20250825Fruits and vegetables play an important role in healthy nutrition of the society and have an important place in the consumer's food pyramid and in meeting the food needs of the society. However, the short shelf life and high perishability of agricultural products, especially citrus fruits and vegetables, have caused undesirable changes, which makes the buyer consume them fresh. The use of healthy preservatives and proper storage, along with maintaining the health and quality of the product, can greatly help in increasing the storage time and reducing spoilage in the products. In the present study, the effect of coating and storage time on post-harvest quality and some quantitative and qualitative characteristics of oranges and lemons in a subzero cold store was investigated.
Introduction
Citrus fruits are one of the most important fruits produced in tropical and horticultural regions of the world. According to estimates, the waste from citrus fruits is approximately between 28 and 31 percent. Therefore, it is of great importance to investigate various methods that will result in reducing the amount of citrus waste. The use of synthetic chemicals in fresh produce is one of the fundamental and important problems in relation to human health, because the residual chemicals, especially in the diet of children and sensitive groups, endanger the health of the people in society, and in view of this, we must look for alternative materials. Today, new technologies have wide applications in the stages of production, processing, storage, packaging and transportation of agricultural products.
Materials and Methods
In this experiment, local blood oranges and lemons were used. These samples were picked from citrus orchards in Sari city on the morning of the experiment. They were brought to the experiment site by observing all the necessary points both during picking and during transportation. For each sample of the coverings, 6 healthy fruits without scratches, fungus, and Mediterranean fly attacks were selected and then wiped with a completely clean cloth and prepared for placement in packaging materials and conducting the necessary tests. It is necessary to explain that the same number of treatments were considered as control treatments. In the treatments that used covering materials, after the treatments were completely covered using a predetermined recipe, they were placed in baskets and transferred to the cold store. For example, for the chitosan treatment, after weighing the number of lemons and oranges with a digital scale and measuring the firmness, soluble solids and pH, the fruit was completely covered with a chitosan solution at a dose of 1 gram (1%) in 1 liter of water and transferred to a cold storage for 60 days at a temperature of 2 to 4 degrees Celsius and a relative humidity of 85%. The rest of the treatments were also treated in the same way and transferred to a cold storage. The weight of all packages was weighed and recorded every 15 days with a digital scale with an accuracy of 0.01. After a 60-day storage period, the percentage of weight loss of the fruits was calculated. The firmness, wheight reduction, soluble solids, titratable acidity, pH and vitamin C of the fruit juice was determined by suggested methods by researchers. In this study, the data were analyzed using a completely randomized design with three replications and a factorial test. Statistical analysis was performed using SPSS software and graphs were drawn using Excel.
Results and Discussion
The results showed that the lowest weight loss, pH change, and vitamin C retention were achieved in the propolis and chitosan coating among the coatings. The firmness of the samples' texture and soluble solids also showed the least changes in the hot water steam, propolis, and chitosan treatments. In general, coating fruits with edible and non-chemical materials improves the quality and shelf life of the product during storage. The use of propolis creates a favorable atmosphere and better preserves the characteristics of citrus fruits. In terms of weight changes, propolis and chitosan coating is the best option for coating compared to other options, which is due to the antibacterial and antifungal properties and the resinous nature of propolis. The use of coating during storage had a significant effect on maintaining the pH of the fruit. In the first 30 days of testing, no change in the pH of the propolis and chitosan samples, water vapor, and chitosan fungicide samples was observed, but in the rest of the cases, changes were observed in the first 30 days. In the second and final 30 days of the test, we had an increase in pH, such that the propolis and chitosan coating had the least change and the ethanol and imizaline coating and the control sample had the most change. Among the coating treatments in orange fruit, edible hot water vapor coatings, propolis, chitosan, and thiabendazole chemical coatings had the least change, which indicates that the use of these coatings, especially propolis and chitosan, has a great effect on preserving soluble solids. The use of coating has a significant effect on maintaining titratable acidity, but the storage period and the interaction effect of coating and storage period on maintaining titratable acidity are not significant. Propolis has a significant function in maintaining titratable acidity in lemon. Propolis controls ethylene synthesis, which causes premature aging and increases respiration rate. A decrease in fruit firmness was observed in all coatings until the end of fruit storage. This phenomenon is due to a decrease in the hydrolysis of cell wall pectins, and this effect is caused by a decrease in the activity and also a decrease in the level of cell wall degrading enzymes or a decrease in the rate of ethylene production due to a decrease in the activity of enzymes involved in ethylene production. Propolis, chitosan, and hot water treatment had the best effect on preserving fruit texture, respectively. Vitamin C decreased during storage, and only chitosan and propolis coatings were able to preserve these values to some extent, but as observed in other parameters, the coating effect on oranges was more favorable than on lemons.
Conclusion
In this study, the physical and chemical properties of two types of local lemon and sanguine orange fruits were investigated using the coating method during the storage period. The results showed that propolis coating performed better than other coatings in preserving the quantitative and qualitative indicators of the fruit and was more effective compared to chemical coatings. Propolis was better than other coatings in all measured traits and had a great impact on preserving the physical and chemical properties of the fruits by controlling respiration, oxidation, delaying the aging process, creating a suitable atmosphere inside the fruit, and better exchange with the cold storage atmosphere. In the next stage, chitosan coating and water vapor for 120 seconds performed better than other coatings. These results clearly state that the use of propolis coating, in addition to preserving the properties of the fruit, is the best option to replace conventional coatings on the market because it is edible, non-toxic, economical, and has the least waste. On the other hand, because chemical fungicides are completely imported and a high cost must be paid to purchase them, and also by saving money, maintenance costs can be reduced and the cost of the product can be reduced, thus bringing health to the consumer community.Fruits and vegetables play an important role in healthy nutrition of the society and have an important place in the consumer's food pyramid and in meeting the food needs of the society. However, the short shelf life and high perishability of agricultural products, especially citrus fruits and vegetables, have caused undesirable changes, which makes the buyer consume them fresh. The use of healthy preservatives and proper storage, along with maintaining the health and quality of the product, can greatly help in increasing the storage time and reducing spoilage in the products. In the present study, the effect of coating and storage time on post-harvest quality and some quantitative and qualitative characteristics of oranges and lemons in a subzero cold store was investigated.
Introduction
Citrus fruits are one of the most important fruits produced in tropical and horticultural regions of the world. According to estimates, the waste from citrus fruits is approximately between 28 and 31 percent. Therefore, it is of great importance to investigate various methods that will result in reducing the amount of citrus waste. The use of synthetic chemicals in fresh produce is one of the fundamental and important problems in relation to human health, because the residual chemicals, especially in the diet of children and sensitive groups, endanger the health of the people in society, and in view of this, we must look for alternative materials. Today, new technologies have wide applications in the stages of production, processing, storage, packaging and transportation of agricultural products.
Materials and Methods
In this experiment, local blood oranges and lemons were used. These samples were picked from citrus orchards in Sari city on the morning of the experiment. They were brought to the experiment site by observing all the necessary points both during picking and during transportation. For each sample of the coverings, 6 healthy fruits without scratches, fungus, and Mediterranean fly attacks were selected and then wiped with a completely clean cloth and prepared for placement in packaging materials and conducting the necessary tests. It is necessary to explain that the same number of treatments were considered as control treatments. In the treatments that used covering materials, after the treatments were completely covered using a predetermined recipe, they were placed in baskets and transferred to the cold store. For example, for the chitosan treatment, after weighing the number of lemons and oranges with a digital scale and measuring the firmness, soluble solids and pH, the fruit was completely covered with a chitosan solution at a dose of 1 gram (1%) in 1 liter of water and transferred to a cold storage for 60 days at a temperature of 2 to 4 degrees Celsius and a relative humidity of 85%. The rest of the treatments were also treated in the same way and transferred to a cold storage. The weight of all packages was weighed and recorded every 15 days with a digital scale with an accuracy of 0.01. After a 60-day storage period, the percentage of weight loss of the fruits was calculated. The firmness, wheight reduction, soluble solids, titratable acidity, pH and vitamin C of the fruit juice was determined by suggested methods by researchers. In this study, the data were analyzed using a completely randomized design with three replications and a factorial test. Statistical analysis was performed using SPSS software and graphs were drawn using Excel.
Results and Discussion
The results showed that the lowest weight loss, pH change, and vitamin C retention were achieved in the propolis and chitosan coating among the coatings. The firmness of the samples' texture and soluble solids also showed the least changes in the hot water steam, propolis, and chitosan treatments. In general, coating fruits with edible and non-chemical materials improves the quality and shelf life of the product during storage. The use of propolis creates a favorable atmosphere and better preserves the characteristics of citrus fruits. In terms of weight changes, propolis and chitosan coating is the best option for coating compared to other options, which is due to the antibacterial and antifungal properties and the resinous nature of propolis. The use of coating during storage had a significant effect on maintaining the pH of the fruit. In the first 30 days of testing, no change in the pH of the propolis and chitosan samples, water vapor, and chitosan fungicide samples was observed, but in the rest of the cases, changes were observed in the first 30 days. In the second and final 30 days of the test, we had an increase in pH, such that the propolis and chitosan coating had the least change and the ethanol and imizaline coating and the control sample had the most change. Among the coating treatments in orange fruit, edible hot water vapor coatings, propolis, chitosan, and thiabendazole chemical coatings had the least change, which indicates that the use of these coatings, especially propolis and chitosan, has a great effect on preserving soluble solids. The use of coating has a significant effect on maintaining titratable acidity, but the storage period and the interaction effect of coating and storage period on maintaining titratable acidity are not significant. Propolis has a significant function in maintaining titratable acidity in lemon. Propolis controls ethylene synthesis, which causes premature aging and increases respiration rate. A decrease in fruit firmness was observed in all coatings until the end of fruit storage. This phenomenon is due to a decrease in the hydrolysis of cell wall pectins, and this effect is caused by a decrease in the activity and also a decrease in the level of cell wall degrading enzymes or a decrease in the rate of ethylene production due to a decrease in the activity of enzymes involved in ethylene production. Propolis, chitosan, and hot water treatment had the best effect on preserving fruit texture, respectively. Vitamin C decreased during storage, and only chitosan and propolis coatings were able to preserve these values to some extent, but as observed in other parameters, the coating effect on oranges was more favorable than on lemons.
Conclusion
In this study, the physical and chemical properties of two types of local lemon and sanguine orange fruits were investigated using the coating method during the storage period. The results showed that propolis coating performed better than other coatings in preserving the quantitative and qualitative indicators of the fruit and was more effective compared to chemical coatings. Propolis was better than other coatings in all measured traits and had a great impact on preserving the physical and chemical properties of the fruits by controlling respiration, oxidation, delaying the aging process, creating a suitable atmosphere inside the fruit, and better exchange with the cold storage atmosphere. In the next stage, chitosan coating and water vapor for 120 seconds performed better than other coatings. These results clearly state that the use of propolis coating, in addition to preserving the properties of the fruit, is the best option to replace conventional coatings on the market because it is edible, non-toxic, economical, and has the least waste. On the other hand, because chemical fungicides are completely imported and a high cost must be paid to purchase them, and also by saving money, maintenance costs can be reduced and the cost of the product can be reduced, thus bringing health to the consumer community.https://jam.tabrizu.ac.ir/article_20981_8cadcaa108660c3f6d05198fa9a25fe7.pdf