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随着全球人口增长和气候变化的加剧，农业生产面临严峻挑战，迫切需要高效、精准的农作物及病虫害识别方法．YOLO算法因其在速度和精度上的优势，成为农业领域的研究热点．首先，系统综述了YOLO算法从v1到v10的演变历程，分析了各版本在网络结构、训练策略、推理效率和精度评估等方面的技术创新和性能提升．其次，通过评估具体的应用案例，探讨了YOLO算法在农作物识别及病虫害检测中的实际表现．最后，总结了当前YOLO算法在农业应用中仍面临轻量化设计、多传感器数据融合、自适应学习、智能标注等挑战，并提出了未来的研究方向．旨在为提升农作物识别及病虫害检测的效率和准确性提供科学依据．
Abstract：With the global population growth and intensifying climate change,agricultural production faces severe challenges, urgently requiring efficient and precise methods for crop and pest identification. The YOLO algorithm has become a research hotspot in agriculture field due to its advantages in speed and accuracy.This paper first provides a systematic review of the evolution of the YOLO algorithm from v1 to v10,and analyzes the technical innovations and performance improvements in network structure,training strategy,inference efficiency,and accuracy assessment in each version.Secondly,the actual performance of the YOLO algorithm in crop identification and pest detection was explored by evaluating specific application cases.Finally,the current challenges in the application of the YOLO algorithm in agriculture were summarized,such as lightweight design,multi-sensor data fusion,adaptive learning,and intelligent annotation,and future research directions were proposed.The aim is to provide scientific evidence for improving the efficiency and accuracy of crop identification and pest detection.
[1]高荣良．人工智能技术在现代农业机械中的应用研究[J]．现代农机，2024(3):121-124.
[2]白玉鹏，冯毅琨，李国厚，等．基于Vision Transformer的小麦病害图像识别算法[J]．中国农机化学报，2024,45(2):267-274.
[3] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:779-788.
[4] FLORES-CALERO M,ASTUDILLO C A,GUEVARA D,et al.Traffic sign detection and recognition using YOLO object detection algorithm:a systematic review[J].Mathematics,2024,12(2):297.
[5] BADGUJAR C M,POULOSE A,GAN H.Agricultural object detection with You Only Look Once(YOLO)Algorithm:a bibliometric and systematic literature review[J].Computers and Electronics in Agriculture,2024,223:109090.
[6] HUSSAIN M.YOLOv1 to v8:Unveiling each variant–a comprehensive review of YOLO[J].IEEE Access,2024,12:42816-42833.
[7] HUSSAIN M.YOLO-v1 to YOLO-v8,the rise of YOLO and its complementary nature toward digital manufacturing and industrial defect detection[J].Machines,2023,11(7):677.
[8] VIJAYAKUMAR A,VAIRAVASUNDARAM S.Yolo-based object detection models:a review and its applications[J].Multimedia Tools and Applications,2024,83:83535–83574.
[9] JIANG P,ERGU D,LIU F,et al.A review of Yolo algorithm developments[J].Procedia Computer Science,2022,199:1066-1073.
[10]耿创，宋品德，曹立佳．YOLO算法在目标检测中的研究进展[J]．兵器装备工程学报，2022,43(9):162-173.
[11]周晋伟，王建平．YOLO物体检测算法研究综述[J]．常州工学院学报，2023,36(1):18-23.
[12] SHAO Y,ZHANG D,CHU H,et al.A review of YOLO object detection based on deep learning[J]．电子与信息学报，2022,44(10):3697-3708.
[13]茅智慧，朱佳利，吴鑫，等．基于YOLO的自动驾驶目标检测研究综述[J]．计算机工程与应用，2022,58(15):68-77.
[14]邓亚平，李迎江．YOLO算法及其在自动驾驶场景中目标检测综述[J]．计算机应用，2024,44(6):1949-1958.
[15]万应霞，燕振刚．基于YOLO算法的农作物病虫害识别研究综述[J]．热带农业工程，2024,48(1):25-28.
[16] ZOU Z,CHEN K,SHI Z,et al.Object detection in 20 years:a survey[J].Proceedings of the IEEE,2023,111(3):257-276.
[17]陈文悦，何军，朱立学，等．基于迁移学习的芒果成熟度分类算法研究[J]．仲恺农业工程学院学报，2022,35(4):56-61.
[18] BAKIRCI M,BAYRAKTAR I.YOLOv9-enabled vehicle detection for urban security and forensics applications[C]//202412th International Symposium on Digital Forensics and Security(ISDFS).San Antonio:IEEE,2024:10527304.
[19] AN R,ZHANG X,SUN M,et al.GC-YOLOv9:innovative smart city traffic monitoring solution[J].Alexandria Engineering Journal,2024,106:277-287.
[20]?ZCAN?，ALTUN Y,PARLAK C.Improving YOLO detection performance of autonomous vehicles in adverse weather conditions using metaheuristic algorithms[J].Applied Sciences,2024,14(13):5841.
[21] TANG Z,XU L,LI H,et al.YOLOC-tiny:a generalized lightweight real-time detection model for multiripeness fruits of large non-green-ripe citrus in unstructured environments[J].Frontiers in Plant Science,2024,15:1415006.
[22] AZIZ F,SAPUTRI D U E.Efficient skin lesion detection using YOLOv9 Network[J].Journal Medical Informatics Technology,2024(1):11-15.
[23] GUI J,WU J,WU D,et al.A lightweight tea buds detection model with occlusion handling[J].Journal of Food Measurement and Characterization,2024,18:7533–7549.
[24] PHAM D,HAN S.Deploying a computer vision model based on YOLOv8 suitable for drones in the tuna fishing and aquaculture industry[J].Journal of Marine Science and Engineering,2024,12(5):828.
[25] XIE W,FENG F,ZHANG H.A detection algorithm for citrus Huanglongbing disease based on an improved YOLOv8n[J].Sensors,2024,24(14):4448.
[26]郭建军，叶俊伟，孔壹右，等．基于深度学习的人体行为识别研究进展[J]．仲恺农业工程学院学报，2024,37(4):55-64.
[27]罗富贵，宋倩，覃运初，等．基于卷积神经网络在图像识别中的应用研究[J]．电脑与信息技术，2024,32(3):51-54.
[28]沈超元，续晋华．深浅双路径卷积神经网络[J]．计算机应用与软件，2024,41(5):298-303.
[29] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Columbus:IEEE,2014:580-587.
[30] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,39(6):1137-1149.
[31] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector[C]//Computer Vision–ECCV 2016:14th European Conference.Amsterdam:Springer,2016:21-37.
[32] WANG A,CHEN H,LIU L,et al.Yolov10:real-time endto-end object detection[EB/OL].(2024-03-23).https://doi.org/10. 48550/arXiv. 2405. 14458.
[33]吴迪，于正林，徐式达，等．基于改进YOLOv5的轴承表面缺陷检测[J]．组合机床与自动化加工技术，2024(6):166-170.
[34] SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:7298594.
[35] BALDI P,SADOWSKI P.The dropout learning algorithm[J].Artificial Intelligence,2014,210:78-122.
[36] ACHILLE A,SOATTO S.Information dropout:learning optimal representations through noisy computation[J].IEEE Transactions on Pattern Analysis And Machine Intelligence,2018,40(12):2897-2905.
[37] SRIVASTAVA N,HINTON G,KRIZHEVSKY A,et al.Dropout:a simple way to prevent neural networks from overfitting[J].The Journal of Machine Learning Research,2014,15(1):1929-1958.
[38] REDMON J,FARHADI A.YOLO9000:better,faster,stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE,2017:7263-7271.
[39] IOFFE S,SZEGEDY C.Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning.Lille:PMLR,2015:448-456.
[40] REDMON J,FARHADI A.Yolov3:an incremental improvement[EB/OL].(2018-04-08).https://doi. org/10. 48550/arXiv. 1804. 02767
[41] MAAS A L,HANNUN A Y,NG A Y.Rectifier nonlinearities improve neural network acoustic models[C]//Proceedings of the30th International Conference on Machine Learning.Atlanta:2013:3.
[42] BOCHKOVSKIY A,WANG C,LIAO H M.Yolov4:optimal speed and accuracy of object detection[EB/OL].(2020-04-23).https://doi. org/10. 48550/arXiv. 2004. 10934.
[43] MISRA D.Mish:a self regularized non-monotonic activation function[EB/OL].(2019-08-23).https://doi. org/10. 48550/arXiv. 1908. 08681.
[44] NELSON J,SOLAWETZ J.Yolov5 is here:State-of-the-art object detection at 140 fps[EB/OL].(2020-06-10).https://blog. roboflow. com/yolov5-is-here.
[45] LI C,LI L,JIANG H,et al.YOLOv6:A single-stage object detection framework for industrial applications[EB/OL].(2022-09-07).https://doi. org/10. 48550/arXiv. 2209. 02976.
[46] WANG C,BOCHKOVSKIY A,LIAO H M.YOLOv7:trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Vancouver:IEEE,2023:7464-7475.
[47] VARGHESE R,SAMBATH M.YOLOv8:a novel object detection algorithm with enhanced performance and robustness[C]//2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems(ADICS).Chennai:IEEE,2024:10533619.
[48] WANG C,YEH I,LIAO H M.Yolov9:learning what you want to learn using programmable gradient information[EB/OL].(2024-02-21).https://doi. org/10. 48550/arXiv. 2402. 13616.
[49] LIN T,DOLLáR P,GIRSHICK R,et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE,2017:2117-2125.
[50] HE K,ZHANG X,REN S,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916.
[51] LIU S,QI L,QIN H,et al.Path aggregation network for instance segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:8759-8768.
[52] ZHENG Z,WANG P,LIU W,et al.Distance-IoU loss:faster and better learning for bounding box regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence.New York:AAAI Press,2020:12993-13000.
[53]李志臣，凌秀军，李鸿秋，等．基于改进YOLOv3树上板栗栗蓬目标检测方法[J]．中国农机化学报，2024,45(11):209-214.
[54]苑迎春，张傲，何振学，等．基于改进YOLOv4-tiny的果园复杂环境下桃果实实时识别[J]．中国农机化学报，2024,45(8):254-261.
[55]梁源林，黄爱清，吴健豪，等．基于改进YOLOv5的菌菇检测及分类模型研究[J]．仲恺农业工程学院学报，2025,38(1):20-27.
[56]帖军，赵捷，郑禄，等．改进YOLOv5模型在自然环境下柑橘识别的应用[J]．中国农业科技导报，2024,26(7):111-120.
[57]马中杰，罗晨，骆巍，等．基于改进YOLOv7-tiny的玉米种质资源雄穗检测方法[J]．农业机械学报，2024,55(7):290-297.
[58]张震，周俊，江自真，等．基于改进YOLOv7轻量化模型的自然果园环境下苹果识别方法[J]．农业机械学报，2024,55(3):231-242.
[59]杨大勇，黄正栎，郑昌贤，等．基于改进YOLOv8n的茶叶嫩稍检测方法[J]．农业工程学报，2024,40(12):165-173.
[60]王金鹏，何萌，甄乾广，等．基于COF-YOLOv8n的油茶果静、动态检测计数[J]．农业机械学报，2024,55(4):193-203.
[61] VO H,MUI K C,THIEN N N,et al.Automating tomato ripeness classification and counting with YOLOv9[J].International Journal of Advanced Computer Science&Applications,2024,15(4):1120-1128.
[62]靳学萌，梁西银，邓鹏飞．基于改进YOLOv10的轻量级黄花菜分级检测模型[J]．智慧农业（中英文），2024,6(5):108-118.
[63]段新涛，王伸，赵晴，等．基于改进YOLOv4的夏玉米主要害虫检测方法研究[J]．山东农业科学，2023,55(10):167-173.
[64]储鑫，李祥，罗斌，等．基于改进YOLOv4算法的番茄叶部病害识别方法[J]．江苏农业学报，2023,39(5):1199-1208.
[65]公徐路，张淑娟．基于改进YOLOv5s的苹果叶片小目标病害轻量化检测方法[J]．农业工程学报，2023,39(19):175-184.
[66]兰玉彬，孙斌书，张乐春，等．基于改进YOLOv5s的自然场景下生姜叶片病虫害识别[J]．农业工程学报，2024,40(1):210-216.
[67]陈禹，吴雪梅，张珍，等．基于改进YOLOv5s的自然环境下茶叶病害识别方法[J]．农业工程学报，2023,39(24):185-194.
[68]刘诗怡，胡滨，赵春．基于改进YOLOv7的黄瓜叶片病虫害检测与识别[J]．农业工程学报，2023,39(15):163-171.
[69]张楠楠，张晓，白铁成，等．基于CBAM-YOLOv7的自然环境下棉叶病虫害识别方法[J]．农业机械学报，2023,54(z1):239-244.
[70]马超伟，张浩，马新明，等．基于改进YOLOv8的轻量化小麦病害检测方法[J]．农业工程学报，2024,40(5):187-195.
[71]庞超，王传安，苏煜，等．基于改进YOLOv8的水稻病害检测方法[J]．内蒙古农业大学学报：自然科学版，2024,45(2):62-68.
[72]李小祥，张洁，秦柯贝，等．基于改进YOLOv8的轻量级复杂环境苹果叶片病害检测方法[J/OL]．南京农业大学学报，2025,48(3):734-743.
基本信息:
中图分类号:TP391.41;S43;S126
引用信息:
[1]曹亮,肖伟,李湘丽.YOLO算法及其在农作物识别及病虫害检测应用综述[J].仲恺农业工程学院学报,2025,38(06):60-71.
基金信息:
国家自然科学基金项目(62373390); 广东省普通高校重点领域专项(2023ZDZX4015); 广东省研究生教育创新计划项目(2024JGXM＿093); 广州市海珠区科技计划项目(海科工商信计2022-36); 广州市农村科技特派员专题项目(2024E04J0154)
2025-04-18
2025-04-18
2025-04-18