University of Tehran PressIranian Journal of Biosystem Engineering2008-480354320230923A study of changes in energy consumption trend and environmental indicators in the production of agricultural crops using a life cycle assessment approach in the years 2018-2022A study of changes in energy consumption trend and environmental indicators in the production of agricultural crops using a life cycle assessment approach in the years 2018-20221189485710.22059/ijbse.2023.364738.665522FAHassanGhasemi MobtakerDepartment of Agricultural Machinery Engineering, Faculty of Agriculture, College of Agriculture and Natural Resources, University of Tehran, Karaj, IranMohammadSharifiDepartment of Agricultural Machinery Engineering, Faculty of Agriculture, College of Agriculture and Natural Resources, University of Tehran, Karaj, IranAliKaabDepartment of Agricultural Machinery Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, IranJournal Article20230903The purpose of this study was to investigate changes in energy consumption and environmental indicators in the production of crops, including sugarcane, wheat, canola and sunflower during the years 2018 to 2022. Relevant information was collected through statistics recorded in planning management of Imam Khomeini Sugarcane Agro-Industrial Company in Khuzestan province. Using these statistics and sources, the production situation of agriculture and industry products was investigated. Based on the obtained results, in sugarcane fields, energy consumption in the years 2018 to 2022 was calculated as 131.05, 142.47, 152.16, 155.45 and 161.71 GJ ha-1 respectively. Also, energy consumption was equal to 30.49, 32.70, 35.10, 36.52, and 38.02 GJ ha-1 in wheat fields in the considered period. On the other hand, for the production of canola in the years 2018 to 2022, it varied and was equal to 19.44, 20.81, 22.68, 23.83 and 24.86 GJ ha-1 respectively. Also, in sunflower fields in the same period of time, it was estimated as 44.19, 82.14, 61.15, 20.16 and 76.16 GJ ha-1 respectively. The results of environmental effects showed that the highest amount of pollution caused by the effect of resources in sugarcane production is in the mentioned years of 12.08, 12.64, 12.91, 12.40 and 12.47 USD2013. Also, in wheat production this index increased from 61.96 to 68.23, in canola production increased from 79.86 to 85.51, and finally, in sunflower production increased from 61.82 to 65.73. Based on the ranking of the by-products, sunflower, wheat and canola are prioritized for cultivation from the first to the third place respectively. Optimal use of electricity inputs, diesel fuel and chemical fertilizers can help reduce energy consumption and environmental pollution. In order to reduce energy consumption in the production of agricultural and industrial products, it is recommended to use renewable energy sources such as electricity produced from solar sources by photovoltaic systems.The purpose of this study was to investigate changes in energy consumption and environmental indicators in the production of crops, including sugarcane, wheat, canola and sunflower during the years 2018 to 2022. Relevant information was collected through statistics recorded in planning management of Imam Khomeini Sugarcane Agro-Industrial Company in Khuzestan province. Using these statistics and sources, the production situation of agriculture and industry products was investigated. Based on the obtained results, in sugarcane fields, energy consumption in the years 2018 to 2022 was calculated as 131.05, 142.47, 152.16, 155.45 and 161.71 GJ ha-1 respectively. Also, energy consumption was equal to 30.49, 32.70, 35.10, 36.52, and 38.02 GJ ha-1 in wheat fields in the considered period. On the other hand, for the production of canola in the years 2018 to 2022, it varied and was equal to 19.44, 20.81, 22.68, 23.83 and 24.86 GJ ha-1 respectively. Also, in sunflower fields in the same period of time, it was estimated as 44.19, 82.14, 61.15, 20.16 and 76.16 GJ ha-1 respectively. The results of environmental effects showed that the highest amount of pollution caused by the effect of resources in sugarcane production is in the mentioned years of 12.08, 12.64, 12.91, 12.40 and 12.47 USD2013. Also, in wheat production this index increased from 61.96 to 68.23, in canola production increased from 79.86 to 85.51, and finally, in sunflower production increased from 61.82 to 65.73. Based on the ranking of the by-products, sunflower, wheat and canola are prioritized for cultivation from the first to the third place respectively. Optimal use of electricity inputs, diesel fuel and chemical fertilizers can help reduce energy consumption and environmental pollution. In order to reduce energy consumption in the production of agricultural and industrial products, it is recommended to use renewable energy sources such as electricity produced from solar sources by photovoltaic systems.https://ijbse.ut.ac.ir/article_94857_7dad7c5189f133936351c5cbc7dc5017.pdfUniversity of Tehran PressIranian Journal of Biosystem Engineering2008-480354320230923Detection of Iranian foods in images using deep learningDetection of Iranian foods in images using deep learning19419561210.22059/ijbse.2023.366560.665526FAZahraHajalioghliFaculty of Agricultural Engineering and Technology, University of Tehran, Karaj, IranSoleimanHosseinpourFaculty of Agricultural Engineering and Technology, University of Tehran, Karaj, IranSeyed SaeidMohtasebiProfessor, Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran0000-0002-4031-1095Journal Article20231012Maintaining the well-being of individuals is greatly influenced by a healthy lifestyle and balanced diet. The identification and segmentation of food items can be improved by utilizing a mobile-based system in this era of rapid lifestyle changes and technology. This article introduces a novel system that, upon receiving input images, detects and segmentation the food items within the images. The system utilizes deep learning techniques and models, employing the YOLO algorithm. By incorporating regression-based simple methods, the system achieves the capability to detect and categorize food items in a single pass through the network, aiming to enhance accuracy and speed in the detection process. YOLOv7 was employed for food detection and YOLOv5, YOLOv7, and YOLOv8 was utilized for image segmentation. Based on the results, the accuracy, recall, and average precision values for YOLOv7 were 0.844, 0.924, and 0.932, respectively. Furthermore, the instance segmentation performance of YOLOv7 outperformed YOLOv5 and YOLOv8, with precision, recall, and mean average precision values of 0.959, 0.943, and 0.906, respectively. These findings underscore the high accuracy in detecting Iranian foods and the remarkable speed and precision in food image segmentation attainable through advanced deep-learning algorithms. Consequently, this study establishes that accurate detection of Iranian foods can be accomplished through the utilization of sophisticated deep-learning techniques. This research focuses on promoting a healthy lifestyle through intelligent technology and novel deep learning algorithms in Iran.Maintaining the well-being of individuals is greatly influenced by a healthy lifestyle and balanced diet. The identification and segmentation of food items can be improved by utilizing a mobile-based system in this era of rapid lifestyle changes and technology. This article introduces a novel system that, upon receiving input images, detects and segmentation the food items within the images. The system utilizes deep learning techniques and models, employing the YOLO algorithm. By incorporating regression-based simple methods, the system achieves the capability to detect and categorize food items in a single pass through the network, aiming to enhance accuracy and speed in the detection process. YOLOv7 was employed for food detection and YOLOv5, YOLOv7, and YOLOv8 was utilized for image segmentation. Based on the results, the accuracy, recall, and average precision values for YOLOv7 were 0.844, 0.924, and 0.932, respectively. Furthermore, the instance segmentation performance of YOLOv7 outperformed YOLOv5 and YOLOv8, with precision, recall, and mean average precision values of 0.959, 0.943, and 0.906, respectively. These findings underscore the high accuracy in detecting Iranian foods and the remarkable speed and precision in food image segmentation attainable through advanced deep-learning algorithms. Consequently, this study establishes that accurate detection of Iranian foods can be accomplished through the utilization of sophisticated deep-learning techniques. This research focuses on promoting a healthy lifestyle through intelligent technology and novel deep learning algorithms in Iran.https://ijbse.ut.ac.ir/article_95612_eed9544a0223602378655475e63b0249.pdfUniversity of Tehran PressIranian Journal of Biosystem Engineering2008-480354320230923Analytical investigation of factors affecting the sustainability of livestock industry development: a case study of Lorestan provinceAnalytical investigation of factors affecting the sustainability of livestock industry development: a case study of Lorestan province43609622710.22059/ijbse.2024.368363.665531FAHadisNematpour Malek AbadDepartment of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.HassanZaki DizajiBiosystems Engineering Dept., Agricultural faculty,
Shahid Chamran University of Ahvaz, Ahvaz, Iran,Journal Article20231203One of the indicators of sustainable development in rural and agricultural fields is the animal husbandry industry. This research presents the method for scientific and appropriate planning for improving and developing livestock production to increase the productivity of livestock production and related industries in order to use the existing potentials, abilities, and capacities optimally. The statistical population consisted of three groups of experts from the Deputy of Livestock Affairs of the Agricultural Jihad Organization, experts from the Animal Science Research Department of the Agricultural Research Center, and representatives of active private sector organizations in Lorestan province. The collection tool was a closed questionnaire consisting of 70 questions prepared in 7 indexes. The results of the ranking of factors and indicators using the TOPSIS technique showed that the hands of the process of supplying inputs and sales and upgrading the activity capacity are the priority and hands of upstream documents, sustainable development plans, mechanisms, perspectives and strategies, and considerations and social requirements and institutions were placed in the following preferences, respectively. By investigating all the sustainable development indicators of the province, Borujerd city, with a ratio of closeness to the ideal option (A*) of 0.79, has won the first rank among the cities of the area. Rumeshgan, Poldokhtar, Azna, Selseleh, Delfan, Chegani, Khorramabad, Aligodarz cities and Durood with the ratio of closeness to the ideal option of 0.76, 0.55, 0.54, 0.53, 0.51, 0.5, 0.46, 0.33 and 0.32 were placed in the following ranks, respectively.One of the indicators of sustainable development in rural and agricultural fields is the animal husbandry industry. This research presents the method for scientific and appropriate planning for improving and developing livestock production to increase the productivity of livestock production and related industries in order to use the existing potentials, abilities, and capacities optimally. The statistical population consisted of three groups of experts from the Deputy of Livestock Affairs of the Agricultural Jihad Organization, experts from the Animal Science Research Department of the Agricultural Research Center, and representatives of active private sector organizations in Lorestan province. The collection tool was a closed questionnaire consisting of 70 questions prepared in 7 indexes. The results of the ranking of factors and indicators using the TOPSIS technique showed that the hands of the process of supplying inputs and sales and upgrading the activity capacity are the priority and hands of upstream documents, sustainable development plans, mechanisms, perspectives and strategies, and considerations and social requirements and institutions were placed in the following preferences, respectively. By investigating all the sustainable development indicators of the province, Borujerd city, with a ratio of closeness to the ideal option (A*) of 0.79, has won the first rank among the cities of the area. Rumeshgan, Poldokhtar, Azna, Selseleh, Delfan, Chegani, Khorramabad, Aligodarz cities and Durood with the ratio of closeness to the ideal option of 0.76, 0.55, 0.54, 0.53, 0.51, 0.5, 0.46, 0.33 and 0.32 were placed in the following ranks, respectively.https://ijbse.ut.ac.ir/article_96227_e78b5cd2b79317981147cae735b0ac2c.pdfUniversity of Tehran PressIranian Journal of Biosystem Engineering2008-480354320230923Distinguishing slivered almonds from peanuts using electronic noseDistinguishing slivered almonds from peanuts using electronic nose61749623610.22059/ijbse.2024.362438.665514FAAliSormilyDepartment of Biosystem Mechanical Engineering, Faculty of Agriculture, Razi University, Kermanshah, Iran.Ali NejatLorestaniDepartment of Biosystem Mechanical Engineering, Faculty of Agriculture, Razi University, Kermanshah, Iran.0000-0002-8221-404XNahidAghili NateghDepartment of Agricultural Machinery Engineering, Sonqor Faculty of Agriculture, Razi University, Kermanshah,IranJournal Article20231101 <br />Almonds are one of the most important types of nuts around the world, which are transformed from a convenient snack to a healthy food for human health.Also, peanut is one of the essential and economic plants in the world, which is very rich in terms of nutrition.The purpose of this research was to investigate the performance of the electronic nose in distinguishing slivered almonds from slivered peanuts.In order to conduct experiments in this research, three types of slivered almond and three types of slivered peanut were used.The samples were tested with an electronic nose made of 10 metal oxide semiconductor (MOS) sensors.In this research, linear discriminant analysis (LDA), principal component analysis (PCA), support vector machine (SVM) and quadratic discriminant analysis (QDA) were used for data analysis. The QDA method with 100% accuracy had a good performance in the classification of slivered almonds varieties and slivered peanuts varieties. Also, the LDA method was able to classify slivered peanuts varieties with 100% accuracy. The LDA method was able to distinguish slivered almonds from slivered peanuts with with an average accuracy 91%. The SVM method was able to distinguish slivered almonds from slivered peanuts with with an average accuracy 84%. The results showed that the lectronic nose is a suitable tool for distinguishing slivered almonds from slivered peanuts. <br />Almonds are one of the most important types of nuts around the world, which are transformed from a convenient snack to a healthy food for human health.Also, peanut is one of the essential and economic plants in the world, which is very rich in terms of nutrition.The purpose of this research was to investigate the performance of the electronic nose in distinguishing slivered almonds from slivered peanuts.In order to conduct experiments in this research, three types of slivered almond and three types of slivered peanut were used.The samples were tested with an electronic nose made of 10 metal oxide semiconductor (MOS) sensors.In this research, linear discriminant analysis (LDA), principal component analysis (PCA), support vector machine (SVM) and quadratic discriminant analysis (QDA) were used for data analysis. The QDA method with 100% accuracy had a good performance in the classification of slivered almonds varieties and slivered peanuts varieties. Also, the LDA method was able to classify slivered peanuts varieties with 100% accuracy. The LDA method was able to distinguish slivered almonds from slivered peanuts with with an average accuracy 91%. The SVM method was able to distinguish slivered almonds from slivered peanuts with with an average accuracy 84%. The results showed that the lectronic nose is a suitable tool for distinguishing slivered almonds from slivered peanuts.https://ijbse.ut.ac.ir/article_96236_cf9ad3391f3f8a115df9dae0062da311.pdfUniversity of Tehran PressIranian Journal of Biosystem Engineering2008-480354320230923Smart Control of a Microrobot for Navigation on Fluid Surface and Simulation of its Application in Microplastics RemovalSmart Control of a Microrobot for Navigation on Fluid Surface and Simulation of its Application in Microplastics Removal75949544110.22059/ijbse.2024.366451.665527FAAmarSalehiDepartment of Agricultural Machinery Engineering, Faculty of Agricultural, College of Agriculture and Natural Resources, University of Tehran, Karaj, IranSoleimanHosseinpourDepartment of Agricultural Machinery Engineering, Faculty of Agricultural, College of Agriculture and Natural Resources, University of Tehran, Karaj, IranNasrollahTabatabaeiDepartment of Medical Nanotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.MahmoudSoltani FirouzDepartment of Agricultural Machinery Engineering, Faculty of Agricultural, College of Agriculture and Natural Resources, University of Tehran, Karaj, IranJournal Article20231017Microplastic contamination of food and beverages has become a global concern in recent years. As a novel approach, magnetic microrobots offer promising potential to address microplastic separation and degradation. However, achieving precise, intelligent, and automated navigation control for microrobots in such tasks remains a significant challenge. This level of control is typically achieved by modeling the complex dynamics of microrobots, the environment, and the actuation system. In this study, an alternative approach was presented using a model-free deep reinforcement learning algorithm (DRL) to navigate a magnetic microrobot on fluid surfaces. In order to simulate the process of reaching a microplastic particle on the fluid surface, the DRL system was implemented to train the microrobot to autonomously navigate from an initial position within the real-world environment to a specified target position. A magnetic actuation system based on two permanent magnets and one-axis Helmholtz coils was constructed to manipulate the position of the microrobot. During the training phase, the microrobot demonstrated high accuracy and speed in achieving the desired position. The evaluation results of the trained model also confirmed the microrobot's success in all episodes, with an average reward of 39.02 out of 40 and a standard deviation of 0.71. These findings indicate that the control system could effectively learn an optimal policy by employing DRL without any prior knowledge of environmental dynamics or the actuation system.Microplastic contamination of food and beverages has become a global concern in recent years. As a novel approach, magnetic microrobots offer promising potential to address microplastic separation and degradation. However, achieving precise, intelligent, and automated navigation control for microrobots in such tasks remains a significant challenge. This level of control is typically achieved by modeling the complex dynamics of microrobots, the environment, and the actuation system. In this study, an alternative approach was presented using a model-free deep reinforcement learning algorithm (DRL) to navigate a magnetic microrobot on fluid surfaces. In order to simulate the process of reaching a microplastic particle on the fluid surface, the DRL system was implemented to train the microrobot to autonomously navigate from an initial position within the real-world environment to a specified target position. A magnetic actuation system based on two permanent magnets and one-axis Helmholtz coils was constructed to manipulate the position of the microrobot. During the training phase, the microrobot demonstrated high accuracy and speed in achieving the desired position. The evaluation results of the trained model also confirmed the microrobot's success in all episodes, with an average reward of 39.02 out of 40 and a standard deviation of 0.71. These findings indicate that the control system could effectively learn an optimal policy by employing DRL without any prior knowledge of environmental dynamics or the actuation system.https://ijbse.ut.ac.ir/article_95441_49777eac4fb09d69cb1feb016a078504.pdfUniversity of Tehran PressIranian Journal of Biosystem Engineering2008-480354320230923Evaluating the environmental impacts of eggplant production in hydroponic greenhouse: implementing a circular bioeconomy approach through composting and biocharEvaluating the environmental impacts of eggplant production in hydroponic greenhouse: implementing a circular bioeconomy approach through composting and biochar951159650210.22059/ijbse.2024.372147.665537FASaeedMofattehDepartment of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, IranMajidKhanaliDepartment of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, IranAsadollahAkramFaculty Member in Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.HomaHosseinzadeh-bandbafhaDepartment of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran0000-0002-1120-9219MaryamAfsharDepartment of Chemistry, Faculty of Science, University of Zanjan, Zanjan, IranJournal Article20240204Despite its merits, hydroponics faces a significant volume of plant residues, including branches, leaves, and stems, after harvesting the main crop. If not managed properly, these residues can provide a suitable substrate for microbial growth, contributing to the proliferation of harmful microorganisms. To address the waste challenge, this research proposes the conversion of residues into compost or biochar based on a circular bioeconomy approach and investigates its environmental sustainability. According to the results, the production of one kilogram of hydroponic eggplant imposes health damages of 1.6×10-6 DALY on humans. Furthermore, in the ecisystems domain, the damage caused by producing one kilogram of hydroponic eggplant is equivalent to 1.08×10-8 species per year while in terms of resources, the estimated damage is $0.261 per kilogram of eggplant. The production and consumption of natural gas in hydroponic eggplant production play a vital role in environmental damages. The circular bioeconomy approach, especially the compost production pathway, there is a 16.6% reduction in health damages. In the ecosystem realm, the compost production pathway contributes to biodiversity conservation with a 16% reduction compared to the biochar production pathway, demonstrating better performance. In the resource damage category, the compost production pathway from green eggplant residues leads to approximately a 16% reduction compared to conventional hydroponic eggplant production. The results highlight the successful performance of eggplant production under the circular bioeconomy approach, focusing on compost production from green residues for environmental improvement and reduction of environmental damages in eggplant production.Despite its merits, hydroponics faces a significant volume of plant residues, including branches, leaves, and stems, after harvesting the main crop. If not managed properly, these residues can provide a suitable substrate for microbial growth, contributing to the proliferation of harmful microorganisms. To address the waste challenge, this research proposes the conversion of residues into compost or biochar based on a circular bioeconomy approach and investigates its environmental sustainability. According to the results, the production of one kilogram of hydroponic eggplant imposes health damages of 1.6×10-6 DALY on humans. Furthermore, in the ecisystems domain, the damage caused by producing one kilogram of hydroponic eggplant is equivalent to 1.08×10-8 species per year while in terms of resources, the estimated damage is $0.261 per kilogram of eggplant. The production and consumption of natural gas in hydroponic eggplant production play a vital role in environmental damages. The circular bioeconomy approach, especially the compost production pathway, there is a 16.6% reduction in health damages. In the ecosystem realm, the compost production pathway contributes to biodiversity conservation with a 16% reduction compared to the biochar production pathway, demonstrating better performance. In the resource damage category, the compost production pathway from green eggplant residues leads to approximately a 16% reduction compared to conventional hydroponic eggplant production. The results highlight the successful performance of eggplant production under the circular bioeconomy approach, focusing on compost production from green residues for environmental improvement and reduction of environmental damages in eggplant production.https://ijbse.ut.ac.ir/article_96502_0809efcf9b7d762c9965f4faf1a7118f.pdf