Computers and Electronics in Agriculture最新文献

{"title":"Path planning and tracking control of orchard wheel mower based on BL-ACO and GO-SMC","authors":"Lixing Liu ,&nbsp;Xu Wang ,&nbsp;Jinyan Xie ,&nbsp;Xiaosa Wang ,&nbsp;Hongjie Liu ,&nbsp;Jianping Li ,&nbsp;Pengfei Wang ,&nbsp;Xin Yang","doi":"10.1016/j.compag.2024.109696","DOIUrl":"10.1016/j.compag.2024.109696","url":null,"abstract":"<div><div>This research proposes an improved ant colony algorithm (BL-ACO) path planning algorithm and a tracking controller based on global optimal sliding mode variable structure control (GO-SMC) for the problem of path planning and tracking control of lawn mowers in quadrilateral orchard environments. The novelty of this research lies in two aspects. On one hand, we analyze the operating scenarios of lawn mowers in standardized orchards, then transform the path planning problem into a traveling salesman problem, and mathematically model the U-shaped and T-shaped turning strategies based on the characteristics of the wheeled lawn mower. In order to make the ant colony algorithm suitable for orchard operation path optimization problems, we modified its pheromone update rules, heuristic functions, state transition probabilities, and other equations. In order to accelerate the convergence speed of the ant colony algorithm, we use the bilayer ant colony algorithm optimization strategy. On the other hand, we establish a kinematic model with the wheeled lawn mower as the control object, and design a control law using a hyperbolic tangent function to ensure the global stability of the trajectory tracking control system. Furthermore, we demonstrate through Lyapunov stability analysis that the GO-SMC controller can ensure the mower tracks the reference path accurately. The simulation experiments of path planning and tracking control show that BL-ACO and GO-SMC perform the best compared to similar algorithms. Field experiments shows that BL-ACO &amp; GO-SMC, with a time reduction rate of 47.58 % and a fuel consumption rate reduction of 47.59 % compared to line by line &amp; SMC.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109696"},"PeriodicalIF":7.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-view-based high-fidelity three-dimensional reconstruction of leaves","authors":"Longfei Wang ,&nbsp;Le Yang ,&nbsp;Huiying Xu ,&nbsp;Xinzhong Zhu ,&nbsp;Wouladje Cabrel ,&nbsp;Golden Tendekai Mumanikidzwa ,&nbsp;Xinyu Liu ,&nbsp;Weijian Jiang ,&nbsp;Hao Chen ,&nbsp;Wenhang Jiang","doi":"10.1016/j.compag.2024.109682","DOIUrl":"10.1016/j.compag.2024.109682","url":null,"abstract":"<div><div>In modern agricultural science research, high-fidelity three-dimensional (3D) leaf models are crucial for crop growth analysis. However, reconstructing the complex morphology and texture of leaves from a single viewpoint under varying natural lighting conditions poses a significant challenge. To address the issues associated with this challenge, this paper presents a diffusion model-based method for single-view leaf reconstruction using potato leaves as the experimental subject. In the camera prediction process, the combination of an explicit point cloud generation technique and an implicit 3D Gaussian rendering technique enables the accurate prediction of camera parameters and the effective capture of leaf phenotypic features. In the synthesis of the 3D model of the leaf, a strategy for optimizing the coarse model UV texture is designed with the objective of achieving spatial consistency of texture details. Furthermore, the model was successfully applied to the reconstruction of other crop leaves and lamellar structural objects, and innovatively constructed a leaf reconstruction model with disease characteristics, aiming to provide a reference for the early 3D detection of crop diseases, as well as a reference for the 3D reconstruction and visualization of other lamellar objects. The results demonstrate that the method is effective in reconstructing the morphological structure and texture details of leaves, as well as thin sheet-like structured objects, achieving fast and high-fidelity single-view reconstruction.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109682"},"PeriodicalIF":7.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and evaluation of a robotic prototype for gerbera harvesting, performing actions at never-seen locations","authors":"Menno Sytsma,&nbsp;Bart M. van Marrewijk,&nbsp;Toon Tielen,&nbsp;Arjan Vroegop,&nbsp;Jos Ruizendaal","doi":"10.1016/j.compag.2024.109671","DOIUrl":"10.1016/j.compag.2024.109671","url":null,"abstract":"<div><div>The harvesting of gerbera flowers, like many horticultural products, is a labor-intensive task for which automated solutions are highly desirable. While robotic harvesting of gerbera flowers has previously been attempted, it has not been tested under commercial greenhouse conditions. This study presents a design process based on realistic requirements derived from detailed measurements of the crop. We introduce a specialized end-effector for gerbera flower harvesting that leverages passive components alongside specific plant characteristics to enable precise positioning and effective cutting. An integrated testing setup is also presented, combining the end-effector with a robust, high-speed sensing and processing pipeline for field trials. Performance evaluations of the complete system under real greenhouse conditions indicate an overall harvest success rate of 78%, with minimal flower collisions and reliable positioning and cutting actions by the end-effector.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109671"},"PeriodicalIF":7.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142721630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the throwing device of residual film recycling machine for the plough layer","authors":"Wanyuan Huang,&nbsp;Haolin Wang,&nbsp;Wei Dai,&nbsp;Ming Zhang,&nbsp;Dezhi Ren,&nbsp;Wei Wang","doi":"10.1016/j.compag.2024.109679","DOIUrl":"10.1016/j.compag.2024.109679","url":null,"abstract":"<div><div>An innovative residual film recycling machine for the plough layer (RFRMPL) is proposed in view of difficulty in picking up residual film and the easy missing out on picking up fine residual film. In this study, the soil throwing device is designed and optimized, as the soil throwing efficiency of the throwing device is essential for residual film separation efficiency of the RFRMPL. The soil throwing efficiency is selected as evaluation index, and a mechanical simulation model of throwing device based on Discrete Element Method (DEM) and Rocky is built up according to structure and working principle of the soil throwing device. The optimal combination of working parameters of the throwing device is obtained via theoretical calculations, single and multi-factorial simulation test. The results show that the optimal working parameters of rotation speed of the rotary tilling mechanism, speed of the soil elevating mechanism and the distance between the rotary tilling mechanism and soil elevating mechanism are 200 rpm, 320 rpm and 130 mm respectively. The field validation test is carried out based on the optimal combination parameters. The results show that soil throwing efficiency of the soil throwing device is 87.45 %. The error between the field validation test results and the simulation results (90.42 %) is 3.4 %, which proves the correctness of the simulation model. It can provide theoretical reference for the design and optimization of the RFRMPL.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109679"},"PeriodicalIF":7.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safflower picking points localization method during the full harvest period based on SBP-YOLOv8s-seg network","authors":"He Zhang,&nbsp;Yun Ge,&nbsp;Hao Xia,&nbsp;Chao Sun","doi":"10.1016/j.compag.2024.109646","DOIUrl":"10.1016/j.compag.2024.109646","url":null,"abstract":"<div><div>Visual recognition is crucial for robotic harvesting of safflower filaments in field. However, accurate detection and localization is challenging due to complex backgrounds, leaves and branches shielding, and variable safflower morphology. This study proposes a safflower picking points localization method during the full harvest period based on SBP-YOLOv8s-seg network. The method enhanced the accuracy by improving the performance of the detection and segmentation network and implementing phased localization. Specifically, SBP-YOLOv8s-seg network based on self-calibration was constructed for precise segmentation of safflower filaments and fruit balls. Additionally, different morphological features of safflower during the full harvest period were analyzed. The segmented masks underwent Principal Component Analysis (PCA) computation, region of interest (ROI) extraction, and contour fitting to extract the principal eigenvectors that express information about the filaments. To address the issue of picking position being invisible due to the occlusion of safflower necking, the picking points were determined in conjunction with the positional relationship between filaments and fruit balls. Experimental results demonstrated that the segmentation performance of SBP-YOLOv8s-seg network was superior to other networks, achieving a significant improvement in mean average precision (mAP) compared to YOLOv5s-seg, YOLOv6s-seg, YOLOv7s-seg, and YOLOv8s-seg, with improvements of 5.1 %, 2.3 %, 4.1 %, and 1.3 % respectively. The precision, recall and mAP of SBP-YOLOv8s-seg network in the segmentation task increased from 87.9 %, 79 %, and 84.4 % of YOLOv8s-seg to 89.1 %, 79.7 %, and 85.7 %. The accuracy of blooming safflower and decaying safflower calculated by the proposed method were 93.0 % and 91.9 %, respectively. The overall localization accuracy of safflower picking points was 92.9 %. Field experiments showed that the picking success rate was 90.7 %. This study provides a theoretical basis and data support for visual localization of safflower picking robot in the future.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109646"},"PeriodicalIF":7.7,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A spatial machine-learning model for predicting crop water stress index for precision irrigation of vineyards","authors":"Aviva Peeters ,&nbsp;Yafit Cohen ,&nbsp;Idan Bahat ,&nbsp;Noa Ohana-Levi ,&nbsp;Eitan Goldshtein ,&nbsp;Yishai Netzer ,&nbsp;Tomás R. Tenreiro ,&nbsp;Victor Alchanatis ,&nbsp;Alon Ben-Gal","doi":"10.1016/j.compag.2024.109578","DOIUrl":"10.1016/j.compag.2024.109578","url":null,"abstract":"<div><div>Optimization of water inputs is possible through precision irrigation based on prescription maps. The crop water stress index (CWSI) is an indicator of spatial and dynamic changes in plant water status that can serve irrigation management decision-making. The driving hypothesis was that in-season CWSI maps based on combined static and spatial-dynamic variables could be used to delineate irrigation MZs. A primary incentive was to minimize thermal-imaging campaigns and to complement CWSI maps between campaigns with cost-effective multi-spectral imaging campaigns producing normalized difference vegetative index (NDVI) maps. A spatial machine-learning model based on a random-forest (RF) algorithm combined with spatial statistical methods was developed to predict the spatial and temporal variability in CWSI of single vines in a vineyard. Model criteria and objectives included the reduction of sample data and input variables to a minimum without impacting prediction accuracy, consideration of only variables readily available to farmers, and accounting for spatial location and spatial processes.</div><div>The model was developed and tested on data from a ‘Cabernet Sauvignon’ vineyard in Israel over two years. Prediction of CWSI was driven by terrain parameters, slope, aspect and topographical wetness index, soil apparent electrical conductivity (ECa), and NDVI.</div><div>Spatial models based on RF were found to support CWSI prediction. Adding a geospatial component significantly improved model performance and accuracy, particularly when raw data was represented as z-scores or when z-scores were used as weights. NDVI, followed by ECa, aspect, or slope, was the most important variable predicting CWSI in the non-spatial models. The stronger the variable importance of NDVI, the better the model performed. The weaker the effect of NDVI in predicting CWSI, the stronger the effect of terrain and soil variables. In the spatial models, based on z-transformed values or on weighted values, the most important variable in predicting CWSI was either NDVI or location.</div><div>The model, based on a limited and readily accessible number of variables, can serve as the basis for user-friendly decision support tools for precision irrigation. Additional research is needed to evaluate alternative prediction variables and to account for case studies in more geographical locations to address overfitting specific input data. Socio-economic and cost-benefit considerations should be integrated to examine whether precision irrigation management based on such models has the desired effects on water consumption and yield.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109578"},"PeriodicalIF":7.7,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating UAV, UGV and UAV-UGV collaboration in future industrialized agriculture: Analysis, opportunities and challenges","authors":"Zhigang Ren,&nbsp;Han Zheng,&nbsp;Jian Chen,&nbsp;Tao Chen,&nbsp;Pengyang Xie,&nbsp;Yunzhe Xu,&nbsp;Jiaming Deng,&nbsp;Huanzhe Wang,&nbsp;Mingjiang Sun,&nbsp;Wenchi Jiao","doi":"10.1016/j.compag.2024.109631","DOIUrl":"10.1016/j.compag.2024.109631","url":null,"abstract":"<div><div>Industrialized agriculture is the direction of future agricultural development, which is developing in the direction of scale, diversification, unmanned and integration. The cooperative operation of UAV, UGV and UAV-UGV is a hot topic in the field of intelligent agricultural multi-machine research. However, at present, most of the research projects have not systematically given the solutions of UAV, UGV and UAV-UGV collaborative application in the future industrialized agriculture. Therefore, we propose the development model of future industrialized agriculture, which derives the key technologies and applications of agricultural UAV, UGV and UAV-UGV collaboration. We summarize and discuss the difficulties and innovative design of the application of UAV, UGV and UAV-UGV collaboration technology in the future industrialized environment, and analyze the opportunities and challenges of the application of UAV, UGV and UAV-UGV collaboration technology in combination with future industrialized agricultural production. Finally, we describe that more technologies (multi-modal sensing technology, embodied intelligent control technology, edge computing technology, end-edge cloud collaborative management and control technology, virtual reality, augmented reality, etc.) are the future research directions for the application of UAV, UGV and UAV-UGV collaboration in industrialized agriculture.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109631"},"PeriodicalIF":7.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of AMIS-optimized vision transformer in identifying disease in Nile Tilapia","authors":"Chutchai Kaewta ,&nbsp;Rapeepan Pitakaso ,&nbsp;Surajet Khonjun ,&nbsp;Thanatkij Srichok ,&nbsp;Peerawat Luesak ,&nbsp;Sarayut Gonwirat ,&nbsp;Prem Enkvetchakul ,&nbsp;Achara Jutagate ,&nbsp;Tuanthong Jutagate","doi":"10.1016/j.compag.2024.109676","DOIUrl":"10.1016/j.compag.2024.109676","url":null,"abstract":"<div><div>Efficient health monitoring in Nile tilapia aquaculture is critical due to the substantial economic losses from diseases, underlining the necessity for innovative monitoring solutions. This study introduces an advanced, automated health monitoring system known as the “Automated System for Identifying Disease in Nile Tilapia (AS-ID-NT),” which incorporates a heterogeneous ensemble deep learning model using the Artificial Multiple Intelligence System (AMIS) as the decision fusion strategy (HE-DLM-AMIS). This system enhances the accuracy and efficiency of disease detection in Nile tilapia. The research utilized two specially curated video datasets, NT-1 and NT-2, each consisting of short videos lasting between 3–10 s, showcasing various behaviors of Nile tilapia in controlled environments. These datasets were critical for training and validating the ensemble model. Comparative analysis reveals that the HE-DLM-AMIS embedded in AS-ID-NT achieves superior performance, with an accuracy of 92.48% in detecting health issues in tilapia. This system outperforms both single model configurations, such as the 3D Convolutional Neural Network and Vision Transformer (ViT-large), which recorded accuracies of 84.64% and 85.7% respectively, and homogeneous ensemble models like ViT-large-Ho and ConvLSTM-Ho, which achieved accuracies of 88.49% and 86.84% respectively. AS-ID-NT provides a non-invasive, continuous, and automated solution for timely intervention, successfully identifying both healthy and unhealthy (infected and environmentally stressed) fish. This system not only demonstrates the potential of advanced AI and machine learning techniques in enhancing aquaculture management but also promotes sustainable practices and food security by maintaining healthier fish populations and supporting the economic viability of tilapia farms.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109676"},"PeriodicalIF":7.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A study of soil modelling methods based on line-structured light—Preparing for the subsoiling digital twin","authors":"Xia Li ,&nbsp;Birong You ,&nbsp;Xuhui Wang ,&nbsp;Zhipeng Zhao ,&nbsp;Tianyu Qi ,&nbsp;Jinyou Xu","doi":"10.1016/j.compag.2024.109685","DOIUrl":"10.1016/j.compag.2024.109685","url":null,"abstract":"<div><div>The virtual model forms the foundation for building a digital twin system; however, methods for modelling dynamically changing soil in subsoiling have not yet been studied. To provide technical guidance for constructing such a system, this study employs a line structured light method for soil model construction. After conducting field and indoor trials, the extreme value method, grayscale centroid method, and Steger algorithm are used to extract the laser centreline. Results indicate that the extreme value method and grayscale centroid method require relatively little processing time—approximately 1.9 ms and 16 ms, respectively—with processing times being nearly the same in different environments. In contrast, the Steger algorithm requires over 300 ms. Regarding memory usage, the three methods demonstrate similar memory consumption when processing images of different environmental conditions: the extreme value method stabilizes at 86.48 MB, the grayscale centroid method at 105.72 MB, and the Steger algorithm fluctuates around 110 MB. The grayscale centroid method exhibits the best stability, making it most suitable for centreline extraction in the digital twin system. During 3D reconstruction, camera capture frequency is positively correlated with reconstruction quality, while movement speed negatively correlates. Each image’s processing time is under 1 ms, showing that the line laser 3D reconstruction method meets the real-time requirements of the digital twin system for subsoiling.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109685"},"PeriodicalIF":7.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A point-based method for identification and counting of tiny object insects in cotton fields","authors":"Mingshuang Bai ,&nbsp;Tao Chen ,&nbsp;Jia Yuan ,&nbsp;Gang Zhou ,&nbsp;Jiajia Wang ,&nbsp;Zhenhong Jia","doi":"10.1016/j.compag.2024.109648","DOIUrl":"10.1016/j.compag.2024.109648","url":null,"abstract":"<div><div>Monitoring of crop pests in the field can be achieved by using sticky traps that capture pests. However, due to the small size and high density of the captured pests, conventional object detection methods relying on bounding boxes struggle to accurately identify and count pests, as they are highly sensitive to positional deviations. Therefore, we propose a novel point framework for multi-species insect identification and counting, termed MS-P2P, which is free from the limitation of Bounding box. Specifically, we employ the lightweight object detection network YOLOv7-tiny for feature extraction and incorporate a lightweight attention detection head (LAHead) for point coordinate regression and insect classification. The LAHead enhances the model’s sensitivity to subtle insect features in complex environments. Additionally, we utilize point proposal prediction and the Hungarian matching algorithm to achieve one-to-one matching of optimal prediction points for targets, which simplifies post-processing methods significantly. Finally, we introduce SmoothL1 Loss and Focal Loss to address the issues of matching instability and class imbalance in the point estimation strategy, respectively. Extensive experiments on the self-built NSC dataset and the publicly available YST dataset have demonstrated the effectiveness of our designed MS-P2P. In particular, on our self-built dataset of 9 insect species, the overall counting metrics achieved a MAE of 18.9 and a RMSE of 28.8. The combined localization and counting metric, nAP0.5, reached 86.4%. Compared with other state-of-the-art algorithms, MS-P2P achieved the best overall results in both localization and counting metrics.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109648"},"PeriodicalIF":7.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}