AgriEngineering最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"Soqia-Advice: A Web-GIS Advisory Platform for Efficient Irrigation in Arboriculture","authors":"Abdelkhalek Ezzahri, Soukaina Boujdi, Mourad Bouziani, Reda Yaagoubi, Lahcen Kenny","doi":"10.3390/agriengineering6020091","DOIUrl":"https://doi.org/10.3390/agriengineering6020091","url":null,"abstract":"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.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"9 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141271731","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}

{"title":"Classification of Soybean Genotypes as to Calcium, Magnesium, and Sulfur Content Using Machine Learning Models and UAV–Multispectral Sensor","authors":"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","doi":"10.3390/agriengineering6020090","DOIUrl":"https://doi.org/10.3390/agriengineering6020090","url":null,"abstract":"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.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"32 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141278019","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}

{"title":"Theoretical Substantiation of the Dependence of Spring Deformation of an Improved Opener","authors":"A. Sarsenov, Zhanna Kubasheva, Adil Ibrayev, A. Sugirbay","doi":"10.3390/agriengineering6020083","DOIUrl":"https://doi.org/10.3390/agriengineering6020083","url":null,"abstract":" 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.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"16 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141102527","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}