Pub Date : 2024-08-09DOI: 10.3390/agriengineering6030162
R. Bist, Xiao Yang, S. Subedi, Bidur Paneru, Lilong Chai
High particulate matter levels in cage-free (CF) houses have led to concerns from producers, as that can pose significant risks to the health and well-being of hens and their caretakers. This study aimed to assess the effectiveness of an electrostatic particle ionization (EPI) + bedding management (BM) treatment in reducing particulate matter (PM) concentrations. Four identical CF rooms each housed 175 hens for six weeks, with two rooms assigned to the EPI + BM treatment (EPI + 20% wood chip topping over 81-week-old litter) and the other two as controls. Measurements of PM were conducted twice a week for 10 min using TSI DustTrak. Additionally, small and large particle concentrations were monitored continuously using a Dylos monitor, with a sampling period of one minute. Footpad scoring was recorded for logistic analysis. Statistical analysis was performed using ANOVA with the Tukey HSD method (p < 0.05). Results demonstrated that the EPI + BM treatment significantly reduced particle counts (37.83% decrease in small particles, 55.90% decrease in large particles) compared to the control group (p < 0.01). PM concentrations were also substantially lowered across different size fractions, ranging from 58.41% to 64.17%. These findings underscore the effectiveness of the EPI + BM treatment in reducing PM in CF houses. The integration of EPI and bedding management innovated in this study holds promise for improving air quality and contributing to the well-being of hens and caretakers in CF housing systems.
{"title":"An Integrated Engineering Method for Improving Air Quality of Cage-Free Hen Housing","authors":"R. Bist, Xiao Yang, S. Subedi, Bidur Paneru, Lilong Chai","doi":"10.3390/agriengineering6030162","DOIUrl":"https://doi.org/10.3390/agriengineering6030162","url":null,"abstract":"High particulate matter levels in cage-free (CF) houses have led to concerns from producers, as that can pose significant risks to the health and well-being of hens and their caretakers. This study aimed to assess the effectiveness of an electrostatic particle ionization (EPI) + bedding management (BM) treatment in reducing particulate matter (PM) concentrations. Four identical CF rooms each housed 175 hens for six weeks, with two rooms assigned to the EPI + BM treatment (EPI + 20% wood chip topping over 81-week-old litter) and the other two as controls. Measurements of PM were conducted twice a week for 10 min using TSI DustTrak. Additionally, small and large particle concentrations were monitored continuously using a Dylos monitor, with a sampling period of one minute. Footpad scoring was recorded for logistic analysis. Statistical analysis was performed using ANOVA with the Tukey HSD method (p < 0.05). Results demonstrated that the EPI + BM treatment significantly reduced particle counts (37.83% decrease in small particles, 55.90% decrease in large particles) compared to the control group (p < 0.01). PM concentrations were also substantially lowered across different size fractions, ranging from 58.41% to 64.17%. These findings underscore the effectiveness of the EPI + BM treatment in reducing PM in CF houses. The integration of EPI and bedding management innovated in this study holds promise for improving air quality and contributing to the well-being of hens and caretakers in CF housing systems.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"89 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.3390/agriengineering6030160
S. A. Mehdizadeh, Allan Lincoln Rodrigues Siriani, Danilo Florentino Pereira
Identifying bird numbers in hostile environments, such as poultry facilities, presents significant challenges. The complexity of these environments demands robust and adaptive algorithmic approaches for the accurate detection and tracking of birds over time, ensuring reliable data analysis. This study aims to enhance methodologies for automated chicken identification in videos, addressing the dynamic and non-standardized nature of poultry farming environments. The YOLOv8n model was chosen for chicken detection due to its high portability. The developed algorithm promptly identifies and labels chickens as they appear in the image. The process is illustrated in two parallel flowcharts, emphasizing different aspects of image processing and behavioral analysis. False regions such as the chickens’ heads and tails are excluded to calculate the body area more accurately. The following three scenarios were tested with the newly modified deep-learning algorithm: (1) reappearing chicken with temporary invisibility; (2) multiple missing chickens with object occlusion; and (3) multiple missing chickens with coalescing chickens. This results in a precise measure of the chickens’ size and shape, with the YOLO model achieving an accuracy above 0.98 and a loss of less than 0.1. In all scenarios, the modified algorithm improved accuracy in maintaining chicken identification, enabling the simultaneous tracking of several chickens with respective error rates of 0, 0.007, and 0.017. Morphological identification, based on features extracted from each chicken, proved to be an effective strategy for enhancing tracking accuracy.
{"title":"Optimizing Deep Learning Algorithms for Effective Chicken Tracking through Image Processing","authors":"S. A. Mehdizadeh, Allan Lincoln Rodrigues Siriani, Danilo Florentino Pereira","doi":"10.3390/agriengineering6030160","DOIUrl":"https://doi.org/10.3390/agriengineering6030160","url":null,"abstract":"Identifying bird numbers in hostile environments, such as poultry facilities, presents significant challenges. The complexity of these environments demands robust and adaptive algorithmic approaches for the accurate detection and tracking of birds over time, ensuring reliable data analysis. This study aims to enhance methodologies for automated chicken identification in videos, addressing the dynamic and non-standardized nature of poultry farming environments. The YOLOv8n model was chosen for chicken detection due to its high portability. The developed algorithm promptly identifies and labels chickens as they appear in the image. The process is illustrated in two parallel flowcharts, emphasizing different aspects of image processing and behavioral analysis. False regions such as the chickens’ heads and tails are excluded to calculate the body area more accurately. The following three scenarios were tested with the newly modified deep-learning algorithm: (1) reappearing chicken with temporary invisibility; (2) multiple missing chickens with object occlusion; and (3) multiple missing chickens with coalescing chickens. This results in a precise measure of the chickens’ size and shape, with the YOLO model achieving an accuracy above 0.98 and a loss of less than 0.1. In all scenarios, the modified algorithm improved accuracy in maintaining chicken identification, enabling the simultaneous tracking of several chickens with respective error rates of 0, 0.007, and 0.017. Morphological identification, based on features extracted from each chicken, proved to be an effective strategy for enhancing tracking accuracy.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"62 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.3390/agriengineering6030161
Gerardo Ortíz-Torres, Manuel A. Zurita-Gil, J. Y. Rumbo-Morales, F. Sorcia-Vázquez, José J. Gascon Avalos, Alan F. Pérez-Vidal, Moises B. Ramos-Martínez, Eric Martínez Pascual, Mario A. Juárez
This paper presents an actuator fault-tolerant control (FTC) strategy for a hexacopter unmanned aerial vehicle (UAV) designed specifically for precision agriculture applications. The proposed approach integrates advanced sensing techniques, including the estimation of Near-Infrared (NIR) reflectance from RGB imagery using the Pix2Pix deep learning network based on conditional Generative Adversarial Networks (cGANs), to enable the calculation of the Normalized Difference Vegetation Index (NDVI) for health assessment. Additionally, trajectory flight planning is developed to ensure the efficient coverage of the targeted agricultural area while considering the vehicle’s dynamics and fault-tolerant capabilities, even in the case of total actuator failures. The effectiveness of the proposed system is validated through simulations and real-world experiments, demonstrating its potential for reliable and accurate data collection in precision agriculture. An NDVI test was conducted on a sugarcane crop using the estimated NIR to assess the crop’s condition during its tillering stage. Therefore, the main contributions this paper include (i) the development of an actuator FTC strategy for a hexacopter UAV in precision agriculture applications, integrating advanced sensing techniques such as NIR reflectance estimation using deep learning network; (ii) the design of a flight trajectory planning method ensuring the efficient coverage of the targeted agricultural area, considering the vehicle’s dynamics and fault-tolerant capabilities; (iii) the validation of the proposed system through simulations and real-world experiments; and (iv) the successful integration of FTC scheme, advanced sensing, and flight trajectory planning for reliable and accurate data collection in precision agriculture.
{"title":"Integrating Actuator Fault-Tolerant Control and Deep-Learning-Based NDVI Estimation for Precision Agriculture with a Hexacopter UAV","authors":"Gerardo Ortíz-Torres, Manuel A. Zurita-Gil, J. Y. Rumbo-Morales, F. Sorcia-Vázquez, José J. Gascon Avalos, Alan F. Pérez-Vidal, Moises B. Ramos-Martínez, Eric Martínez Pascual, Mario A. Juárez","doi":"10.3390/agriengineering6030161","DOIUrl":"https://doi.org/10.3390/agriengineering6030161","url":null,"abstract":"This paper presents an actuator fault-tolerant control (FTC) strategy for a hexacopter unmanned aerial vehicle (UAV) designed specifically for precision agriculture applications. The proposed approach integrates advanced sensing techniques, including the estimation of Near-Infrared (NIR) reflectance from RGB imagery using the Pix2Pix deep learning network based on conditional Generative Adversarial Networks (cGANs), to enable the calculation of the Normalized Difference Vegetation Index (NDVI) for health assessment. Additionally, trajectory flight planning is developed to ensure the efficient coverage of the targeted agricultural area while considering the vehicle’s dynamics and fault-tolerant capabilities, even in the case of total actuator failures. The effectiveness of the proposed system is validated through simulations and real-world experiments, demonstrating its potential for reliable and accurate data collection in precision agriculture. An NDVI test was conducted on a sugarcane crop using the estimated NIR to assess the crop’s condition during its tillering stage. Therefore, the main contributions this paper include (i) the development of an actuator FTC strategy for a hexacopter UAV in precision agriculture applications, integrating advanced sensing techniques such as NIR reflectance estimation using deep learning network; (ii) the design of a flight trajectory planning method ensuring the efficient coverage of the targeted agricultural area, considering the vehicle’s dynamics and fault-tolerant capabilities; (iii) the validation of the proposed system through simulations and real-world experiments; and (iv) the successful integration of FTC scheme, advanced sensing, and flight trajectory planning for reliable and accurate data collection in precision agriculture.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"49 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-10DOI: 10.3390/agriengineering6020095
Christina Sebald, Maximilian Treiber, Esmahan Eryilmaz, Heinz Bernhardt
The application of digital technologies in the agricultural sector is increasing. One of the new key technologies is the Internet of Things (IoT), which can facilitate the everyday work of farmers. For the successful adoption of IoT-enabled digital products and to ensure improved workflows, the usability of human–machine interfaces is highly important. Various design approaches of human–machine interfaces (HMIs) can currently be found in the monitoring of agricultural machinery. In this work, the most well-known HMIs are considered. Based on a usability test (participants n = 9), the user interface (UI) of a novel mobile application (NEVONEX Cockpit App) was chosen as an example of a design approach of an IoT ecosystem that is oriented towards the UI design of mobile applications. This work aims to identify the weak points of this UI. Conclusions about the needs, and thus an improvement of the user experience, are based on the suggestions for improvement and the information about the general requirements of farmers for a UI for agricultural machinery. Here, it was found that most farmers are satisfied with the UI design of their familiar tractor monitors. According to the survey, the three most important points to be considered in the UI design are intuitive operation and menu navigation, easy access to the essential functions and buttons, and sufficiently large control panels. The conducted usability tests can be considered a basis for developing HMIs for agriculture machinery. Through repeated execution of comparable usability tests, there is the possibility of developing future HMI guidelines for agriculture to improve the user experience (UX). For the NEVONEX ecosystem, feedback from the user interface testing was incorporated in a major revision of the Cockpit App’s design, where a lot more display space was given to the agronomic digital services by smartly arranging infrastructure functions in tiles.
数字技术在农业领域的应用日益增多。新的关键技术之一是物联网（IoT），它可以为农民的日常工作提供便利。要成功采用物联网数字产品并确保改进工作流程，人机界面的可用性非常重要。目前，在农业机械监控领域可以找到各种人机界面（HMI）的设计方法。在这项工作中，考虑了最著名的人机界面。基于可用性测试（参与者 n = 9），选择了一款新型移动应用程序（NEVONEX 驾驶舱应用程序）的用户界面（UI）作为物联网生态系统设计方法的范例，该设计方法以移动应用程序的用户界面设计为导向。这项工作旨在找出该用户界面的薄弱点。根据改进建议和农民对农业机械用户界面的一般要求，得出有关需求的结论，从而改善用户体验。调查发现，大多数农民对他们熟悉的拖拉机显示器的用户界面设计感到满意。调查显示，用户界面设计中最需要考虑的三个要点是：直观的操作和菜单导航、方便使用基本功能和按钮以及足够大的控制面板。所进行的可用性测试可作为开发农业机械人机界面的基础。通过反复进行类似的可用性测试，有可能制定出未来的农业人机界面指南，以改善用户体验（UX）。在 NEVONEX 生态系统中，用户界面测试的反馈意见被纳入驾驶舱应用程序设计的重大修订中，通过将基础设施功能巧妙地安排在磁贴中，为农艺数字服务提供了更多的显示空间。
{"title":"Usability Testing of Novel IoT-Infused Digital Services on Farm Equipment Reveals Farmer’s Requirements towards Future Human–Machine Interface Design Guidelines","authors":"Christina Sebald, Maximilian Treiber, Esmahan Eryilmaz, Heinz Bernhardt","doi":"10.3390/agriengineering6020095","DOIUrl":"https://doi.org/10.3390/agriengineering6020095","url":null,"abstract":"The application of digital technologies in the agricultural sector is increasing. One of the new key technologies is the Internet of Things (IoT), which can facilitate the everyday work of farmers. For the successful adoption of IoT-enabled digital products and to ensure improved workflows, the usability of human–machine interfaces is highly important. Various design approaches of human–machine interfaces (HMIs) can currently be found in the monitoring of agricultural machinery. In this work, the most well-known HMIs are considered. Based on a usability test (participants n = 9), the user interface (UI) of a novel mobile application (NEVONEX Cockpit App) was chosen as an example of a design approach of an IoT ecosystem that is oriented towards the UI design of mobile applications. This work aims to identify the weak points of this UI. Conclusions about the needs, and thus an improvement of the user experience, are based on the suggestions for improvement and the information about the general requirements of farmers for a UI for agricultural machinery. Here, it was found that most farmers are satisfied with the UI design of their familiar tractor monitors. According to the survey, the three most important points to be considered in the UI design are intuitive operation and menu navigation, easy access to the essential functions and buttons, and sufficiently large control panels. The conducted usability tests can be considered a basis for developing HMIs for agriculture machinery. Through repeated execution of comparable usability tests, there is the possibility of developing future HMI guidelines for agriculture to improve the user experience (UX). For the NEVONEX ecosystem, feedback from the user interface testing was incorporated in a major revision of the Cockpit App’s design, where a lot more display space was given to the agronomic digital services by smartly arranging infrastructure functions in tiles.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":" 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141366614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-05DOI: 10.3390/agriengineering6020094
Juan Luis Valenzuela, José Gregorio Díaz, M. C. Salas-Sanjuán
Saffron cultivation is important in global agriculture and is mainly flourishing in Mediterranean climates. Although it originated in Asia Minor, it is widely grown in regions such as Iran, India, Spain, Morocco, Greece, and Italy. Labour-intensive harvesting, mainly by hand, characterises its production and offers substantial employment opportunities in cultivating areas. However, traditional saffron-producing countries such as Spain, Italy, and Greece have witnessed declining production due to labour demands and competition from low-wage countries. Mechanization remains unfeasible due to the delicate nature of the plant. To revitalise saffron cultivation, efforts have been focused on reducing labour costs, improving productivity, and improving quality through innovative techniques, such as soilless crops. In this study, the productivity and quality of saffron was evaluated in a soilless culture and three fertigation doses were evaluated: a control, consisting of Sonneveld and Voogt’s standard nutrient solution, and two treatments with two supplemented solutions, 30% K and 30% Ca. The results indicated that the solution with 30% K obtained higher corm productivity, as well as better quality saffron, as all the products of this treatment were included in Category I according to the ISO 3632 standard, while the quality of saffron grown with the control solution was lower.
藏红花种植在全球农业中占有重要地位，主要在地中海气候条件下蓬勃发展。虽然藏红花起源于小亚细亚，但在伊朗、印度、西班牙、摩洛哥、希腊和意大利等地区广泛种植。劳动密集型采摘（主要是手工采摘）是藏红花生产的特点，为种植区提供了大量就业机会。然而，由于劳动力需求和来自低工资国家的竞争，西班牙、意大利和希腊等传统藏红花生产国的藏红花产量不断下降。由于藏红花植物的娇嫩特性，机械化仍然不可行。为了振兴藏红花种植，人们一直致力于通过无土栽培等创新技术降低劳动力成本、提高生产率和改善质量。在这项研究中，对无土栽培藏红花的生产率和质量进行了评估，并对三种施肥剂量进行了评估：一种是由 Sonneveld 和 Voogt 的标准营养液组成的对照组，另一种是两种添加了 30% K 和 30% Ca 的溶液的处理组。结果表明，添加 30% K 的溶液可获得更高的花蕾产量和更好的藏红花质量，因为根据 ISO 3632 标准，该处理的所有产品都属于 I 类，而使用对照溶液种植的藏红花质量较低。
{"title":"Improvement in Productivity and Quality of Soilless Saffron Crops by Implementing Fertigation","authors":"Juan Luis Valenzuela, José Gregorio Díaz, M. C. Salas-Sanjuán","doi":"10.3390/agriengineering6020094","DOIUrl":"https://doi.org/10.3390/agriengineering6020094","url":null,"abstract":"Saffron cultivation is important in global agriculture and is mainly flourishing in Mediterranean climates. Although it originated in Asia Minor, it is widely grown in regions such as Iran, India, Spain, Morocco, Greece, and Italy. Labour-intensive harvesting, mainly by hand, characterises its production and offers substantial employment opportunities in cultivating areas. However, traditional saffron-producing countries such as Spain, Italy, and Greece have witnessed declining production due to labour demands and competition from low-wage countries. Mechanization remains unfeasible due to the delicate nature of the plant. To revitalise saffron cultivation, efforts have been focused on reducing labour costs, improving productivity, and improving quality through innovative techniques, such as soilless crops. In this study, the productivity and quality of saffron was evaluated in a soilless culture and three fertigation doses were evaluated: a control, consisting of Sonneveld and Voogt’s standard nutrient solution, and two treatments with two supplemented solutions, 30% K and 30% Ca. The results indicated that the solution with 30% K obtained higher corm productivity, as well as better quality saffron, as all the products of this treatment were included in Category I according to the ISO 3632 standard, while the quality of saffron grown with the control solution was lower.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"19 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-05DOI: 10.3390/agriengineering6020093
J. P. A. R. Cunha, L. D. L. Lopes, C. B. Alvarenga
The application of pesticides using unmanned aerial vehicles (UAVs) has grown, but there is a lack of information to support more efficient applications. Using a DJI AGRAS-MG-1P octocopter equipped with different spray tips, this study sought to explore spray deposition (leaves and fruit) and efficacy of chlorpyrifos on control of coffee berry borer at different spray volumes and flight heights. The study was conducted in an Arabica coffee plantation. The study consisted of eight treatments and four replications in a 2 × 2 × 2 factorial scheme: two flight heights (1.5 and 3.0 m), two spray tips (hollow cone and flat fan), and two spray volumes (10 and 15 L ha−1). Deposition was assessed by detecting a tracer in the coffee leaves and fruit using spectrophotometry. The coffee berry borer-control efficacy trial was conducted in two areas by evaluating the percentage of damaged fruit 60 days after two insecticide applications. The flight height of 1.5 m promoted higher spray deposition on leaves and fruit and a lower incidence of damaged fruit. Flat fan spray tips resulted in higher spray deposition on the leaves, not interfering with the deposition on fruit or the coffee berry borer control. Increasing the spray volume from 10 to 15 L ha−1 did not increase spray deposition on coffee leaves and fruit. Chlorpyrifos applied via UAVs reduced the incidence of coffee berry borer.
{"title":"Chemical Control of Coffee Berry Borer Using Unmanned Aerial Vehicle under Different Operating Conditions","authors":"J. P. A. R. Cunha, L. D. L. Lopes, C. B. Alvarenga","doi":"10.3390/agriengineering6020093","DOIUrl":"https://doi.org/10.3390/agriengineering6020093","url":null,"abstract":"The application of pesticides using unmanned aerial vehicles (UAVs) has grown, but there is a lack of information to support more efficient applications. Using a DJI AGRAS-MG-1P octocopter equipped with different spray tips, this study sought to explore spray deposition (leaves and fruit) and efficacy of chlorpyrifos on control of coffee berry borer at different spray volumes and flight heights. The study was conducted in an Arabica coffee plantation. The study consisted of eight treatments and four replications in a 2 × 2 × 2 factorial scheme: two flight heights (1.5 and 3.0 m), two spray tips (hollow cone and flat fan), and two spray volumes (10 and 15 L ha−1). Deposition was assessed by detecting a tracer in the coffee leaves and fruit using spectrophotometry. The coffee berry borer-control efficacy trial was conducted in two areas by evaluating the percentage of damaged fruit 60 days after two insecticide applications. The flight height of 1.5 m promoted higher spray deposition on leaves and fruit and a lower incidence of damaged fruit. Flat fan spray tips resulted in higher spray deposition on the leaves, not interfering with the deposition on fruit or the coffee berry borer control. Increasing the spray volume from 10 to 15 L ha−1 did not increase spray deposition on coffee leaves and fruit. Chlorpyrifos applied via UAVs reduced the incidence of coffee berry borer.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"22 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-04DOI: 10.3390/agriengineering6020092
R. Fanigliulo, W. Stefanoni, L. Fornaciari, R. Grilli, Stefano Benigni, Daniela Scutaru, G. Sperandio, D. Pochi
Wood fuel from the agroforestry sector is one of the main strategies cited by the EU for reducing energetic dependance on foreign markets. Its sustainability, both economic and environmental, can be improved through the optimization of harvesting and chipping operations. This study was focused on the dynamic and energetic balance of the chipping phase carried out by a chipper operated by the power-take-off (PTO) of a medium-power tractor. Both machines were equipped with sensors for real-time monitoring of fuel consumption, PTO torque and speed, trunk diameter and working time during the comminution of 61 poplar trees grown in a medium rotation coppice system. The data analysis was carried out on the entire dataset (about 29,000 records) without considering their belonging to different trees. By means of proper data ordinations, it has been possible to define all the intervals in which the chipping stopped (e.g., between two trees) and to exclude them from the intervals of actual chipping. This has allowed forcomputation of operative and actual working time, as well as of the basic power required to operate the chipper and the power for actual chipping. Subsequently, the parameter values observed during actual chipping were related to the cutting diameters measured at the same instant. Subsequently, the dataset was divided according to seven diameter classes, and, for each class, the descriptive statistical indices of working time, work productivity, CO2 emissions, energy requirement and fuel consumption were calculated. Eventually, the correlation between the variations in trunk diameter and other parameters was verified both on the whole dataset and based on the class average values. The analysis made it possible to identify the conditions of greatest efficiency for the chipper. More generally, the method could help to increase the accuracy of measurements aimed at characterizing the performance of chippers from the point of view of dynamic energy requirements as well as in relation to different wood species.
{"title":"Proposal of an Original Methodology to Evaluate the Performance of Chipper Machines","authors":"R. Fanigliulo, W. Stefanoni, L. Fornaciari, R. Grilli, Stefano Benigni, Daniela Scutaru, G. Sperandio, D. Pochi","doi":"10.3390/agriengineering6020092","DOIUrl":"https://doi.org/10.3390/agriengineering6020092","url":null,"abstract":"Wood fuel from the agroforestry sector is one of the main strategies cited by the EU for reducing energetic dependance on foreign markets. Its sustainability, both economic and environmental, can be improved through the optimization of harvesting and chipping operations. This study was focused on the dynamic and energetic balance of the chipping phase carried out by a chipper operated by the power-take-off (PTO) of a medium-power tractor. Both machines were equipped with sensors for real-time monitoring of fuel consumption, PTO torque and speed, trunk diameter and working time during the comminution of 61 poplar trees grown in a medium rotation coppice system. The data analysis was carried out on the entire dataset (about 29,000 records) without considering their belonging to different trees. By means of proper data ordinations, it has been possible to define all the intervals in which the chipping stopped (e.g., between two trees) and to exclude them from the intervals of actual chipping. This has allowed forcomputation of operative and actual working time, as well as of the basic power required to operate the chipper and the power for actual chipping. Subsequently, the parameter values observed during actual chipping were related to the cutting diameters measured at the same instant. Subsequently, the dataset was divided according to seven diameter classes, and, for each class, the descriptive statistical indices of working time, work productivity, CO2 emissions, energy requirement and fuel consumption were calculated. Eventually, the correlation between the variations in trunk diameter and other parameters was verified both on the whole dataset and based on the class average values. The analysis made it possible to identify the conditions of greatest efficiency for the chipper. More generally, the method could help to increase the accuracy of measurements aimed at characterizing the performance of chippers from the point of view of dynamic energy requirements as well as in relation to different wood species.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"67 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.3390/agriengineering6020091
Abdelkhalek Ezzahri, Soukaina Boujdi, Mourad Bouziani, Reda Yaagoubi, Lahcen Kenny
The determination of water requirements for crops holds a crucial role in optimizing irrigation and enhancing agricultural productivity. However, identifying these needs remains a significant challenge due to the variety of factors influencing this decision, such as meteorological conditions, soil structure, and the phenological stages of each crop. In this study, we propose the design and development of a dedicated web-based irrigation advisory platform for arboriculture named ‘Soqia-Advice’. This platform will provide services to farmers, advisors, and decision-makers. The proposed methodology is based on four main steps: (1) need assessments; (2) definition of functionalities to fulfill these needs; (3) design of the overall architecture and the conceptual data model; and (4) implementation of key features of the module dedicated to farmers. The prototype of the “Farmer” module was tested on a farm in Azrou city, Morocco, as a case study. Seven-day weather forecasts were seamlessly integrated using the Weatherbit API. Additionally, the irrigation schedule was accurately displayed, ensuring efficient water management. Functionality tests were conducted on each menu to ensure the seamless and reliable operation of all planned features. The results were rigorously assessed to ensure that each feature aligned with the identified needs.
确定作物需水量对于优化灌溉和提高农业生产率至关重要。然而，由于气象条件、土壤结构和每种作物的物候期等影响因素多种多样，确定这些需求仍然是一项重大挑战。在本研究中，我们建议设计和开发一个专门的网络灌溉咨询平台，用于树艺，名为 "Soqia-Advice"。该平台将为农民、顾问和决策者提供服务。建议的方法基于四个主要步骤：(1) 需求评估；(2) 确定满足这些需求的功能；(3) 设计整体架构和概念数据模型；(4) 实现农民专用模块的主要功能。作为案例研究，"农民 "模块的原型在摩洛哥阿兹鲁市的一个农场进行了测试。使用 Weatherbit API 无缝集成了七天天气预报。此外，还准确显示了灌溉计划，确保了高效的用水管理。对每个菜单都进行了功能测试，以确保所有计划功能的无缝和可靠运行。测试结果经过严格评估，以确保每项功能都符合已确定的需求。
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Pub Date : 2024-06-01DOI: 10.3390/agriengineering6020090
D. C. Santana, Izabela Cristina de Oliveira, Sâmela Beutinger Cavalheiro, Paulo Henrique Menezes das Chagas, M. C. T. Teixeira Filho, João Lucas Della-Silva, L. Teodoro, C. N. S. Campos, F. Baio, C. A. da Silva Junior, P. Teodoro
Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field.
使植物育种计划（尤其是大豆育种计划）成本更低、更快、更实用、更准确，可促进大豆新基因型的选育，有助于培育出吸收和代谢养分效率更高的新品种。利用大豆基因型的光谱信息与次要常量营养素的营养信息相结合，可帮助遗传改良计划选择能有效吸收和代谢这些营养素的种群。此外，利用机器学习算法处理这些信息，可以更准确地获得优良基因型。因此，这项工作的目的是通过 ML 算法和不同的输入验证大豆基因型在次要常量营养素方面的分类性能。实验在巴西南马托格罗索联邦大学位于南查帕当市的实验区进行。大豆在 2019/20 作季播种，种植了 103 个 F2 大豆种群。实验设计采用随机区组，两次重复。在作物出苗后 60 天（DAE），使用 Sensifly eBee RTK 固定翼遥控飞机（RPA）采集光谱图像，该飞机具有自主起飞控制、飞行计划和着陆功能。在生殖期（R1），每株采集三片叶子，以确定钙（Ca）、镁（Mg）和硫（S）等宏量营养素的含量。根据光谱信息和基因型在钙、镁和硫方面的营养价值所获得的数据进行了皮尔逊相关性分析；使用 k-means 算法进行了 PC 分析，将基因型划分为聚类。聚类作为输出变量，而光谱数据则作为机器学习分析中分类模型的输入变量。模型中测试的配置包括光谱带（SB）、植被指数（VI）以及两者的组合。机器学习算法与光谱数据的结合可提供有关大豆植物的重要生物信息。根据钙、镁和硫含量对大豆基因型进行分类，可最大限度地节省遗传改良计划中田间评估的时间、精力和人力。因此，在随机森林算法中使用光谱带作为输入数据，可以高效地对大豆基因型进行次生宏量营养元素分类，这对实地研究人员来说非常重要。
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Pub Date : 2024-05-24DOI: 10.3390/agriengineering6020083
A. Sarsenov, Zhanna Kubasheva, Adil Ibrayev, A. Sugirbay
The article presents factors influencing the germination and development of plants after seeding with disk seeders. Schemes of improved two-disk seeders are proposed, forces acting on the improved seeder during operation, determination of the maximum distance between the seeder disks at the field surface level, and calculation schemes for determining the draft resistance of the serial and improved seeders, the area of the flat disk segment of the seeder, determination of the deformer, and tailstock area of the pressing plate. During the theoretical study of the seeding process, the following parameters and observations were obtained: analytical dependencies of soil density created by the pressing plate; geometric parameters of the pressing plate with a curvature radius r = 52…57 mm, plate section thickness of 2.5 mm; installation of the pressing plate insignificantly increases the draft resistance of the seeder; and the depth of the seeder’s travel has the greatest influence on spring deformation. Experimental studies reveal that the stiffness of the pressing plate is 7500…7600 N/m, ensuring an optimal furrow bottom density of 1.1–1.3 g/cm3; in the range of seed embedding depth of 0.05…0.07 m, 89% of the total number of seeds are placed compared to 76% of seeds embedded by the serial seeder.
文章介绍了使用盘式播种机播种后影响植物发芽和生长的因素。文章提出了改进型双盘播种机的方案、工作期间作用在改进型播种机上的力，确定了播种机圆盘之间在田面水平的最大距离，以及确定串联和改进型播种机牵引阻力、播种机平盘段面积、变形器的确定和压种板尾座面积的计算方案。在对播种过程进行理论研究期间，获得了以下参数和观察结果：压土板产生的土壤密度的分析依赖关系；压土板的几何参数，曲率半径 r = 52...57 毫米，板截面厚度为 2.5 毫米；安装压土板对播种机牵引阻力的增加不明显；播种机的行程深度对弹簧变形的影响最大。实验研究表明，压种板的刚度为 7500...7600 N/m，可确保最佳沟底密度为 1.1-1.3 g/cm3；在种子嵌入深度为 0.05...0.07 m 的范围内，播下的种子占种子总数的 89%，而串行播种机嵌入的种子占 76%。
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