[1]李仁杰,宋涛,高婕,等.基于改进YOLOv5的自然环境下番茄患病叶片检测模型[J].江苏农业学报,2024,(06):1028-1037.[doi:doi:10.3969/j.issn.1000-4440.2024.06.009]
　LI Renjie,SONG Tao,GAO Jie,et al.Tomato diseased leaf detection model based on improved YOLOv5 in natural environment[J].,2024,(06):1028-1037.[doi:doi:10.3969/j.issn.1000-4440.2024.06.009]
点击复制

基于改进YOLOv5的自然环境下番茄患病叶片检测模型()
分享到：

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2024年06期
页码:
1028-1037
栏目:
农业信息工程
出版日期:
2024-06-30

文章信息/Info

Title:
Tomato diseased leaf detection model based on improved YOLOv5 in natural environment
作者:
李仁杰; 宋涛; 高婕; 李东; 高鹏; 李炳鑫; 杨坡
（河北工业大学电子信息工程学院,天津300000）
Author(s):
LI Renjie; SONG Tao; GAO Jie; LI Dong; GAO Peng; LI Bingxin; YANG Po
(School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300000, China)
关键词:
病害检测; 深度学习; YOLOv5; 注意力机制
Keywords:
disease detection; deep learning; YOLOv5; attention mechanism
分类号:
TP391；S641.2
DOI:
doi:10.3969/j.issn.1000-4440.2024.06.009
摘要:
针对自然环境下番茄叶片存在的复杂背景和密集遮挡情况,提出一种改进的YOLOv5模型,用于实时检测自然环境下番茄叶片的病害。首先,使用RepVGG模块代替YOLOv5中主干网络的卷积层,改善主干网络的特征提取能力,减少模型的内存占用,加速模型的推理速度;其次,在颈部的C3模块中引入注意力机制模块CBAM,提高模型在复杂背景下对番茄患病叶片的检测精度以及对遮挡目标的识别率;最后,引用新的损失函数SIoU,加速模型的收敛速度并降低模型的损失值。研究结果表明,相比于原YOLOv5模型,改进模型的平均精度提升3.0个百分点,平均精度高达98.9%,说明改进模型在自然环境下对番茄患病叶片的检测更具优势。
Abstract:
Aiming at the complex background and dense occlusion of tomato leaves in natural environment, an improved YOLOv5 model was proposed for real-time detection of tomato leaf diseases in natural environment. Firstly, the RepVGG module was used to replace the convolutional layer of the backbone network in YOLOv5, which improved the feature extraction capability of the backbone network, reduced the memory occupation of the model and accelerated the reasoning speed of the model. Secondly, the attention mechanism module CBAM was introduced into C3 module in neck part to improve the detection accuracy of tomato diseased leaves and the recognition rate of shielded targets in the complex background. Finally, a new loss function SIoU was introduced to accelerate the convergence speed of the model and reduce the loss value of the model. The research results showed that compared with the original YOLOv5 model, the average precision of the improved model increased by three percentage points, and the average accuracy was as high as 98.9%, indicating that the improved model was more advantageous in the detection of tomato diseased leaves in natural environment.

参考文献/References:

[1]周蓉,赵统敏,赵丽萍,等. 高光泽番茄育种及果实表面光泽度[J]. 江苏农业学报,2018,34（6）:1437-1440.
[2]LI Y, NIE J, CHAO X. Do we really need deep CNN for plant diseases identification[J]. Computers and Electronics in Agriculture,2020,178:105803.
[3]周惠汝,吴波明. 深度学习在作物病害图像识别方面应用的研究进展[J]. 中国农业科技导报,2021,23(5):61-68.
[4]OZGUVEN M M, ADEM K. Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms[J]. Physica A:Statistical Mechanics and Its Applications,2019,535:122537.
[5]WAHEED A, GOYAL M, GUPTA D, et al. An optimized dense convolutional neural network model for disease recognition and classification in corn leaf[J]. Computers and Electronics in Agriculture,2020,175:105456.
[6]LIU B, DING Z, TIAN L, et al. Grape leaf disease identification using improved deep convolutional neural networks[J]. Frontiers in Plant Science,2020,11:1082.
[7]邵明月,张建华,冯全,等. 深度学习在植物叶部病害检测与识别的研究进展[J]. 智慧农业(中英文),2022,4(1):29-46.
[8]张志远,罗铭毅,郭树欣,等. 基于改进YOLOv5的自然环境下樱桃果实识别方法[J].农业机械学报,2022,53(增刊1):232-240.
[9]SINGH D, JAIN N, JAIN P, et al. PlantDoc: a dataset for visual plant disease detection[M]. New York:ACM,2020:249-253.
[10]LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation[C]. Salt Lake City:IEEE,2018.
[11]DING X, ZHANG X, MA N, et al. RepVGG:making vgg-style convnets great again[C]. Nashville:IEEE,2021.
[12]孙龙清,王泊宁,王嘉煜,等. 基于G-RepVGG和鱼类运动行为的水质监测方法[J].农业机械学报,2022,53(增刊2):210-218.
[13]WOO S, PARK J, LEE J Y, et al. CBAM:convolutional block attention module[C]. Berlin:Springer,2018.
[14]ZHENG Z, WANG P, LIU W, et al. Distance-IoU loss: faster and better learning for bounding box regression[C]. Menlo Park:AAAI,2020.
[15]YU J, JIANG Y, WANG Z, et al. Unitbox:an advanced object detection network[C]. New York:ACM,2016.
[16]GEVORGYAN Z. SIoU loss:more powerful learning for bounding box regression[J]. ArXiv Preprint ArXiv,2022. DOI:10.48550/arXiv.2205.12740.
[17]HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]. Nashville:IEEE,2021.
[18]HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]. Piscataway:IEEE,2018.
[19]WANG Q, WU B, ZHU P, et al. ECA-Net:efficient channel attention for deep convolutional neural networks[C]. Seattle:IEEE,2020.
[20]REZATOFIGHI H, TSOI N, GWAK J Y, et al. Generalized intersection over union: a metric and a loss for bounding box regression[C]. Long Beach:IEEE,2019.
[21]ZHANG Y, REN W, ZHANG Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing,2022,506:146-157.
[22]LIU W, ANGUELOV D, ERHAN D, et al. SSD:single shot multibox detector[C]. Berlin:Springer,2016.
[23]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, 2017, 39(6):1137-1149.
[24]REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. ArXiv Preprint ArXiv,2018. DOI:10.48550/arXiv.1804.02767.

相似文献/References:

[1]任胜男,孙钰,张海燕,等.基于one-shot学习的小样本植物病害识别[J].江苏农业学报,2019,(05):1061.[doi:doi:10.3969/j.issn.1000-4440.2019.05.009]
　REN Sheng-nan,SUN Yu,ZHANG Hai-yan,et al.Plant disease identification for small sample based on one-shot learning[J].,2019,(06):1061.[doi:doi:10.3969/j.issn.1000-4440.2019.05.009]
[2]宋晓倩,张学艺,张春梅,等.基于深度迁移学习的酿酒葡萄种植信息提取[J].江苏农业学报,2020,(03):689.[doi:doi:10.3969/j.issn.1000-4440.2020.03.022]
　SONG Xiao-qian,ZHANG Xue-yi,ZHANG Chun-mei,et al.Extraction of wine grape planting information based on deep transfer learning[J].,2020,(06):689.[doi:doi:10.3969/j.issn.1000-4440.2020.03.022]
[3]金寿祥,周宏平,姜洪喆,等.采摘机器人视觉系统研究进展[J].江苏农业学报,2023,(02):582.[doi:doi:10.3969/j.issn.1000-4440.2023.02.033]
　JIN Shou-xiang,ZHOU Hong-ping,JIANG Hong-zhe,et al.Research progress on visual system of picking robot[J].,2023,(06):582.[doi:doi:10.3969/j.issn.1000-4440.2023.02.033]
[4]李颀,郭梦媛.基于深度学习的休眠期苹果树点云语义分割[J].江苏农业学报,2023,(05):1189.[doi:doi:10.3969/j.issn.1000-4440.2023.05.011]
　LI Qi,GUO Meng-yuan.Semantic segmentation of apple tree point cloud in dormant period based on deep learning[J].,2023,(06):1189.[doi:doi:10.3969/j.issn.1000-4440.2023.05.011]
[5]翟先一,魏鸿磊,韩美奇,等.基于改进YOLO卷积神经网络的水下海参检测[J].江苏农业学报,2023,(07):1543.[doi:doi:10.3969/j.issn.1000-4440.2023.07.011]
　ZHAI Xian-yi,WEI Hong-lei,HAN Mei-qi,et al.Underwater sea cucumber identification based on improved YOLO convolutional neural network[J].,2023,(06):1543.[doi:doi:10.3969/j.issn.1000-4440.2023.07.011]
[6]许鑫,耿庆,郑凯,等.基于纹理特征与深度学习的小麦图像中的穗粒分割与计数[J].江苏农业学报,2024,(04):661.[doi:doi:10.3969/j.issn.1000-4440.2024.04.010]
　XU Xin,GENG Qing,ZHENG Kai,et al.Segmentation and counting of wheat spikes and grains based on texture features and deep learning[J].,2024,(06):661.[doi:doi:10.3969/j.issn.1000-4440.2024.04.010]

备注/Memo

备注/Memo:
收稿日期：2023-04-24基金项目：河北省重点研发计划项目(22370701D)作者简介：李仁杰(1997-),男,安徽芜湖人,硕士研究生,研究方向为计算智能与无线网络。(E-mail)2270464695@qq.com通讯作者：宋涛,(E-mail)songtao@hebut.edu.cn
更新日期/Last Update: 2024-07-15