AgriEngineering最新文献

{"title":"Technological Upgrade of a Vicon RS-EDW Spreader: Development of a Microcontroller for Variable Rate Application","authors":"J. Serrano, Alexandre Amaral, S. Shahidian, José Marques da Silva, Francisco J. Moral, Carlos Escribano","doi":"10.3390/agriengineering6020082","DOIUrl":"https://doi.org/10.3390/agriengineering6020082","url":null,"abstract":"Over the last two decades, a considerable amount of equipment has been acquired (spreaders, seeders, sprayers, among others) to respond to the challenges of the precision agriculture (PA) concept. Most of this equipment has been purchased at a high cost. However, many of them, despite still being functional and equipped with sensors, actuators, and electronic processing units capable of adjusting to variations in speed, have become obsolete in terms of communication and incompatible with new monitoring and control systems based on the “Isobus” protocol. This work aims to present a solution for updating the control system (“Ferticontrol”) of a “Vicon RS-EDW” spreader with variable rate application (VRA), making it compatible with the “InCommand” system from “Ag Leader”. The solution includes serial protocol mediation using low-cost tools such as “Arduino” and “Raspberry Pi” microcontrollers and open-source software. The development shows that it is possible to implement a solution that is accessible to farmers in general. It also provides a niche business opportunity for young researchers to set up small technology-based enterprises associated with universities and research centers. These partnerships guarantee permanent innovation and represent a decisive step towards modern, technological, competitive, and sustainable agriculture.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"8 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113101","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":"Spray Angle and Uniformity of the Flat Fan Nozzle of Deep Loosener Fertilizer for Intra-Soil Application of Fertilizers","authors":"S. Nukeshev, Khozhakeldi Tanbayev, M. Ramaniuk, N. Kakabayev, A. Sugirbay, Aidar Moldazhanov","doi":"10.3390/agriengineering6020079","DOIUrl":"https://doi.org/10.3390/agriengineering6020079","url":null,"abstract":"This paper deals with the problem of predetermining the spray angle and uniformity of the flat fan sprayer with a semicircular impact surface for the intra-soil application of liquid mineral fertilizers. The jet impact on a round splash plate and radial atomization properties are investigated theoretically, the formation features of the spray with an obtuse angle are studied in a geometrical way, and the design search of the nozzle shape and optimization calculations are performed using computational fluid dynamics (CFD) simulations and then verified experimentally. It was revealed that the spray rate and spray angle can be adjusted by changing the parameter s, and when the spray angle is within s = 0–0.2 mm, it forms spray angles with range of 140°–175°. The spraying angle, in turn, shows the potential length of the tillage knife in accordance with the undersoil cavity dimensions. A spray uniformity of up to 74% was achieved, which is sufficient for applied studies and for intra-soil application operations. According to the investigations and field experiments, it can be concluded that the designed nozzle is applicable for the intra-soil application of liquid mineral fertilizers. The use of flat fan nozzles that form a spraying band under the soil cavity and along the entire length of the tillage knife ensures a highly efficient mixing process, the liquid mineral fertilizers with treated soil (particles) positively contributing to plant maturation.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141119919","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":"Health and Thermal Comfort of Dairy Cattle in Compost-Bedded Pack Barns and Other Types of Housing: A Comparative Systematic Review","authors":"C. E. A. Oliveira, I. F. F. Tinôco, F. C. Sousa, F. C. Baêta, F. Vieira, M. Barbari","doi":"10.3390/agriengineering6020080","DOIUrl":"https://doi.org/10.3390/agriengineering6020080","url":null,"abstract":"This systematic review was conducted to describe and discuss the main research findings available in the literature concerning the health and thermal comfort of dairy cattle housed in Compost-Bedded Pack Barn (CBP) systems, in comparison to Free Stall (FS), Tie-Stall (TS), and/or Loose Housing (LH) systems. Searches for peer-reviewed experimental articles in English were performed in the Scopus and Web of Science databases. Forty-three non-duplicated scientific articles were obtained and subjected to a four-stage evaluation process, according to the PRISMA methodology and predefined eligibility criteria. This process resulted in the selection of 13 articles for inclusion. Regarding animal health, the results provide evidence that the incidence of problems such as lameness, limb injuries, and reproductive disorders is lower in CBP systems. However, if bedding management is not effective in ensuring the provision of dry and comfortable surfaces, an increase in somatic cell count (SCC) and prevalence of mastitis incidence (PMI) may occur. For thermal comfort, it was found that the CBP system exhibited higher temperatures during summer and lower temperatures during winter when compared to FS with cross-ventilation in association with evaporative cooling. However, no differences were observed in terms of thermal comfort in spring and autumn. As this is a recent research area, caution should be exercised when extrapolating the results, considering the specificities of each cited study.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"74 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141121269","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":"Machine Learning-Based Control of Autonomous Vehicles for Solar Panel Cleaning Systems in Agricultural Solar Farms","authors":"Farima Hajiahmadi, Mohammad Jafari, Mahmut Reyhanoglu","doi":"10.3390/agriengineering6020081","DOIUrl":"https://doi.org/10.3390/agriengineering6020081","url":null,"abstract":"This paper presents a machine learning (ML)-based approach for the intelligent control of Autonomous Vehicles (AVs) utilized in solar panel cleaning systems, aiming to mitigate challenges arising from uncertainties, disturbances, and dynamic environments. Solar panels, predominantly situated in dedicated lands for solar energy production (e.g., agricultural solar farms), are susceptible to dust and debris accumulation, leading to diminished energy absorption. Instead of labor-intensive manual cleaning, robotic cleaners offer a viable solution. AVs equipped to transport and precisely position these cleaning robots are indispensable for the efficient navigation among solar panel arrays. However, environmental obstacles (e.g., rough terrain), variations in solar panel installation (e.g., height disparities, different angles), and uncertainties (e.g., AV and environmental modeling) may degrade the performance of traditional controllers. In this study, a biologically inspired method based on Brain Emotional Learning (BEL) is developed to tackle the aforementioned challenges. The developed controller is implemented numerically using MATLAB-SIMULINK. The paper concludes with a comparative analysis of the AVs’ performance using both PID and developed controllers across various scenarios, highlighting the efficacy and advantages of the intelligent control approach for AVs deployed in solar panel cleaning systems within agricultural solar farms. Simulation results demonstrate the superior performance of the ML-based controller, showcasing significant improvements over the PID controller.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"37 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120257","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":"Applying YOLOv8 and X-ray Morphology Analysis to Assess the Vigor of Brachiaria brizantha cv. Xaraés Seeds","authors":"Daniel de Amaral da Silva, Emannuel Diego Gonçalves de Freitas, Haynna Fernandes Abud, Danielo G. Gomes","doi":"10.3390/agriengineering6020050","DOIUrl":"https://doi.org/10.3390/agriengineering6020050","url":null,"abstract":"Seed quality significantly affects how well crops grow. Traditional methods for checking seed quality, like seeing how many seeds sprout or using a chemical test called tetrazolium testing, require people to look at the seeds closely, which takes a lot of time and effort. Nowadays, computer vision, a technology that helps computers see and understand images, is being used more in farming. Here, we use computer vision with X-ray imaging to assist experts in rapidly and accurately assessing seed quality. We looked at three different sets of seeds using X-ray images and used YOLOv8 to analyze them. YOLOv8 software measures different aspects about seeds, like their size and the area taken up by the part inside, called the endosperm. Based on this information, we put the seeds into four groups depending on how much endosperm they have. Our results show that the YOLOv8 program works well in identifying and separating the endosperm, even with a small amount of data. Our method was able to accurately identify the endosperm about 95.6% of the time. This means that our approach can help determine how effective the seeds are to plant crops.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":" 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140211000","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":"Evaluation of a System to Assess Herbicide Movement in Straw under Dry and Wet Conditions","authors":"Izabela Thais dos Santos, Ivana Paula Ferraz Santos de Brito, Ana Karollyna Alves De Matos, Valesca Pinheiro de Miranda, Guilherme Constantino Meirelles, Priscila Oliveira de Abreu, R. Alcántara-de la Cruz, E. D. Velini, C. A. Carbonari","doi":"10.3390/agriengineering6010049","DOIUrl":"https://doi.org/10.3390/agriengineering6010049","url":null,"abstract":"Straw from no-till cropping systems, in addition to increasing the soil organic matter content, may also impede the movement of applied herbicides into the soil and, thus, alter the behavior and fate of these compounds in the environment. Rain or irrigation before or after an herbicide treatment can either help or hinder its movement through the straw, influencing weed control. Our objective was to develop a system for herbicide application and rain simulation, enabling the evaluation of the movement of various herbicides either in dry or wet straw under different rainfall volumes (25, 50, 75, and 100 mm). The amount of the applied herbicides that moved through the straw were collected and measured using a liquid chromatograph with a tandem mass spectrometry system (LC-MS/MS). Measurements obtained with the developed system showed a high herbicide treatment uniformity across all replications. The movement of the active ingredients through the straw showed variability that was a function of the applied herbicide, ranging from 17% to 99%. In wet straw, the collected herbicide remained constant from 50 to 100 mm of simulated rainfall. For the wet straw, the decreasing percentages of the herbicide movement through straw to the soil were sulfentrazone (99%), atrazine and diuron (91% each), hexazinone (84%), fomesafen (80.4%), indaziflam (79%), glyphosate (63%), haloxyfop-p-methyl (45%), and S-metolachlor (27%). On the dry straw, the decreasing percentages of the herbicide movement were fomesafen (88%), sulfentrazone (74%), atrazine (69.4%), hexazinone (69%), diuron (68.4%), glyphosate (48%), indaziflam (34.4%), S-metolachlor (22%), and haloxyfop-p-methyl (18%). Overall, herbicide movement was higher in wet straw (with a previous 25 mm simulated rainfall layer) than in dry straw. Some herbicides, like haloxyfop-p-methyl and indaziflam, exhibited over 50% higher movement in wet straw than dry straw after 100 mm of simulated rain. The developed system can be adapted for various uses, serving as a valuable tool to evaluate the behavior of hazardous substances in different agricultural and environmental scenarios.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"19 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140229667","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":"A Performance Comparison of CNN Models for Bean Phenology Classification Using Transfer Learning Techniques","authors":"Teodoro Ibarra-Pérez, Ramón Jaramillo-Martínez, H. C. Correa-Aguado, Christophe Ndjatchi, Ma. del Rosario Martínez-Blanco, H. A. Guerrero-Osuna, F. Mirelez-Delgado, J. I. Casas-Flores, Rafael Reveles-Martínez, U. A. Hernández-González","doi":"10.3390/agriengineering6010048","DOIUrl":"https://doi.org/10.3390/agriengineering6010048","url":null,"abstract":"The early and precise identification of the different phenological stages of the bean (Phaseolus vulgaris L.) allows for the determination of critical and timely moments for the implementation of certain agricultural activities that contribute in a significant manner to the output and quality of the harvest, as well as the necessary actions to prevent and control possible damage caused by plagues and diseases. Overall, the standard procedure for phenological identification is conducted by the farmer. This can lead to the possibility of overlooking important findings during the phenological development of the plant, which could result in the appearance of plagues and diseases. In recent years, deep learning (DL) methods have been used to analyze crop behavior and minimize risk in agricultural decision making. One of the most used DL methods in image processing is the convolutional neural network (CNN) due to its high capacity for learning relevant features and recognizing objects in images. In this article, a transfer learning approach and a data augmentation method were applied. A station equipped with RGB cameras was used to gather data from images during the complete phenological cycle of the bean. The information gathered was used to create a set of data to evaluate the performance of each of the four proposed network models: AlexNet, VGG19, SqueezeNet, and GoogleNet. The metrics used were accuracy, precision, sensitivity, specificity, and F1-Score. The results of the best architecture obtained in the validation were those of GoogleNet, which obtained 96.71% accuracy, 96.81% precision, 95.77% sensitivity, 98.73% specificity, and 96.25% F1-Score.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"119 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140233919","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":"An Effective and Affordable Internet of Things (IoT) Scale System to Measure Crop Water Use","authors":"J. Payero","doi":"10.3390/agriengineering6010047","DOIUrl":"https://doi.org/10.3390/agriengineering6010047","url":null,"abstract":"Scales are widely used in many agricultural applications, ranging from weighing crops at harvest to determine crop yields to regularly weighing animals to determine growth rate. In agricultural research applications, there is a long history of measuring crop water use (evapotranspiration [ET]) using a particular type of scale called weighing lysimeters. Typically, weighing lysimeters require very accurate data logging systems that tend to be expensive. Recent developments in open-source technologies, such as micro-controllers and Internet of Things (IoT) platforms, have created opportunities for developing effective and affordable ways to monitor crop water use and transmit the data to the Internet in near real-time. Therefore, this study aimed to create an affordable Internet of Things (IoT) scale system to measure crop ET. A scale system to monitor crop ET was developed using an Arduino-compatible microcontroller with cell phone communication, electronic load cells, an Inter-Integrated Circuit (I2C) multiplexer, and analog-to-digital converters (ADCs). The system was powered by a LiPo battery, charged by a small (6 W) solar panel. The IoT scale system was programmed to collect data from the load cells at regular time intervals and send the data to the ThingSpeak IoT platform. The system performed successfully during indoor and outdoor experiments conducted in 2023 at the Clemson University Edisto Research and Education Center, Blackville, SC. Calibrations relating the measured output of the scale load cells to changes in mass resulted in excellent linear relationships during the indoor (r2 = 1.0) and outdoor experiments (r2 = 0.9994). The results of the outdoor experiments showed that the IoT scale system could accurately measure changes in lysimeter mass during several months (Feb to Jun) without failure in data collection or transmission. The changes in lysimeter mass measured during that period reflected the same trend as concurrent soil moisture data measured at a nearby weather station. The changes in lysimeter mass measured with the IoT scale system during the outdoor experiment were accurate enough to derive daily and hourly crop ET and even detect what appeared to be dew formation during the morning hours. The IoT scale system can be built using open-source, off-the-shelf electronic components which can be purchased online and easily replaced or substituted. The system can also be developed at a fraction of the cost of data logging, communication, and visualization systems typically used for lysimeter and scale applications.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"11 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247550","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":"Robotic Multi-Boll Cotton Harvester System Integration and Performance Evaluation","authors":"Shekhar Thapa, Glen C. Rains, Wesley M. Porter, Guoyu Lu, Xianqiao Wang, Canicius J. Mwitta, S. Virk","doi":"10.3390/agriengineering6010046","DOIUrl":"https://doi.org/10.3390/agriengineering6010046","url":null,"abstract":"Several studies on robotic cotton harvesters have designed their end-effectors and harvesting algorithms based on the approach of harvesting a single cotton boll at a time. These robotic cotton harvesting systems often have slow harvesting times per boll due to limited computational speed and the extended time taken by actuators to approach and retract for picking individual cotton bolls. This study modified the design of the previous version of the end-effector with the aim of improving the picking ratio and picking time per boll. This study designed and fabricated a pullback reel to pull the cotton plants backward while the rover harvested and moved down the row. Additionally, a YOLOv4 cotton detection model and hierarchical agglomerative clustering algorithm were implemented to detect cotton bolls and cluster them. A harvesting algorithm was then developed to harvest the cotton bolls in clusters. The modified end-effector, pullback reel, vacuum conveying system, cotton detection model, clustering algorithm, and straight-line path planning algorithm were integrated into a small red rover, and both lab and field tests were conducted. In lab tests, the robot achieved a picking ratio of 57.1% with an average picking time of 2.5 s per boll. In field tests, picking ratio was 56.0%, and it took an average of 3.0 s per boll. Although there was no improvement in the lab setting over the previous design, the robot’s field performance was significantly better, with a 16% higher picking ratio and a 46% reduction in picking time per boll compared to the previous end-effector version tested in 2022.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"761 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140246830","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 Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms","authors":"M. Gumma, Ramavenkata Mahesh Nukala, P. Panjala, P. Bellam, Snigdha Gajjala, S. K. Dubey, Vinay Kumar Sehgal, Ismail Mohammed, K. C. Deevi","doi":"10.3390/agriengineering6010045","DOIUrl":"https://doi.org/10.3390/agriengineering6010045","url":null,"abstract":"This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"74 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140251698","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}