基于深度学习的农作物病虫害识别研究进展

doi:10.14088/j.cnki.issn0439-8114.2023.11.029

湖北农业科学 ›› 2023, Vol. 62 ›› Issue (11): 165-169.doi: 10.14088/j.cnki.issn0439-8114.2023.11.029

基于深度学习的农作物病虫害识别研究进展

李正, 李宝喜, 李志豪, 战艺芳, 王利华, 龚琦

武汉市农业科学院环境与安全研究所,武汉 430207

收稿日期:2023-06-07 出版日期:2023-11-25 发布日期:2023-12-25
通讯作者: 李宝喜（1965-）,男,湖北武汉人,正高级农艺师,主要从事农产品质量安全研究,（电话）027-81705205（电子信箱）834244513@qq.com。
作者简介:李正（1996-）,女,湖北十堰人,硕士,主要从事农业信息化研究,（电话）027-81705205（电子信箱）lizheng_hubeiwuhan@foxmail.com。
基金资助:
湖北省自然科学基金项目（2022CFB777）

Research progress in crop disease and pest identification based on deep learning

LI Zheng, LI Bao-xi, LI Zhi-hao, ZHAN Yi-fang, WANG Li-hua, GONG Qi

Institute of Environment and Safety, Wuhan Academy of Agricultural Sciences, Wuhan 430207,China

Received:2023-06-07 Online:2023-11-25 Published:2023-12-25

摘要/Abstract

摘要： 为了对农作物病虫害进行有效防治、有效保障农作物健康状况,快速、精准地识别农作物病虫害是有效防治的前提条件。对农作物病虫害识别研究进行了综述,归纳了农作物病虫害识别方法的发展历程,重点分析了深度学习的网络结构、建模重点环节及6类典型架构特征,并结合当下的研究热点和应用前景,从构建公共数据集、集成多种成像技术、优化大模型性能等方向进行展望。

关键词: 深度学习, 农作物病虫害, 图像识别, 有效防治

Abstract: In order to effectively prevent and control crop diseases and pests, and ensure crop health, rapid and accurate identification of crop diseases and pests was a prerequisite for effective prevention and control.A review was conducted on the research on crop pest and disease identification, summarizing the development process of crop pest and disease identification methods. The focus was on analyzing the network structure, modeling key links, and six typical architectural features of deep learning. Combined with current research hotspots and application prospects, prospects were made from the construction of public datasets, integration of multiple imaging technologies, and optimization of large model performance.

Key words: deep learning, crop diseases and pests, image recognition, effective prevention and control

中图分类号:

S126

李正, 李宝喜, 李志豪, 战艺芳, 王利华, 龚琦. 基于深度学习的农作物病虫害识别研究进展[J]. 湖北农业科学, 2023, 62(11): 165-169.

LI Zheng, LI Bao-xi, LI Zhi-hao, ZHAN Yi-fang, WANG Li-hua, GONG Qi. Research progress in crop disease and pest identification based on deep learning[J]. HUBEI AGRICULTURAL SCIENCES, 2023, 62(11): 165-169.

参考文献

[1] 赵彦茜,肖登攀,唐建昭,等.气候变化对我国主要粮食作物产量的影响及适应措施[J].水土保持研究,2019,26(6):317-326.
[2] 刘杰,曾娟,杨清坡,等.2023年农作物重大病虫害发生趋势预报[J].中国植保导刊,2023,43(1):32-35.
[3] 翟肇裕,曹益飞,徐焕良,等.农作物病虫害识别关键技术研究综述[J].农业机械学报,2021,52(7):1-18.
[4] SUYKENS J, VANDEWALLE J.Least squares support vector machine classifiers[J]. Neural processing letters,1999,9(3):293-300.
[5] CORTES C, VAPNIK V.Support-vector networks[J]. Machine learning, 1995, 20(3): 273-297.
[6] SARO J,KAVIT A.Review:Study on simple K-mean and modified K-mean clustering technique[J].International journal of computer science engineering and technology,2016,6(7):279-281.
[7] PEARSON K.On lines and planes of closest fit to systems of points in space[J]. Philosophical magazine,1901,2(11),559-572.
[8] 郑建华,朱立学,朱蓉.基于多特征融合与支持向量机的葡萄病害识别[J].现代农业装备,2018(6):54-60.
[9] 雷雨,韩德俊,曾庆东,等.基于高光谱成像技术的小麦条锈病病害程度分级方法[J].农业机械学报,2018,49(5):226-232.
[10] 乔雪,潘新,王欣宇,等.基于G-R分量与K-means的马铃薯病虫害图像分割[J].内蒙古农业大学学报(自然科学版),2021,42(3):84-87.
[11] 何怀文,蔡显华,杨毅红.基于深度学习的园林植物病虫害智能识别系统[J].现代计算机,2022,28(15):112-116.
[12] 高雨亮. 基于深度学习的水稻病虫害识别系统设计与实现[D].江苏扬州:扬州大学,2022.
[13] 赵越,赵辉,姜永成,等.基于深度学习的马铃薯叶片病害检测方法[J].中国农机化学报,2022,43(10):183-189.
[14] SCHMIDHUBER J.Deep learning in neural networks:An overview[J]. Neural networks,2014,61:85-117.
[15] LESHNO M, LIN V Y,PINKUS A,et al.Multilayer feedforward networks with a nonpolynomial activation function can approximate any function[J].Neural networks,1993,6(6):861-867.
[16] DU X,VASUDEVAN R, JOHNSON-ROBERSON M.Bio-LSTM: A biomechanically inspired recurrent neural network for 3-D pedestrian pose and gait prediction[J].IEEE robotics and automation letters, 2019, 4(2):1501-1508.
[17] TETILA E C, MACHADO B B, MENEZES G K, et al.Automatic recognition of soybean leaf diseases using UAV images and deep convolutional neural networks[J]. IEEE geoscience and remote sensing letters, 2019, 17(5):903-907.
[18] 李子涵,周省邦,赵戈,等.基于卷积神经网络的农业病虫害识别研究综述[J].江苏农业科学,2023,51(7):15-23.
[19] 郑丽丽. 基于决策树与卷积神经网络的害虫识别算法[J].洛阳师范学院学报,2020,39(5):32-35.
[20] 魏超,范自柱,张泓,等.基于深度学习的农作物病害检测[J].江苏大学学报(自然科学版),2019,40(2):190-196.
[21] 姚建斌,张英娜,刘建华.基于卷积神经网络和迁移学习的小麦病虫害识别[J].华北水利水电大学学报(自然科学版),2022,43(2):102-108.
[22] LECUN Y, BOTTOU L, BENGIO Y, et al.Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[23] 何前,郭峰林,方皓正,等.基于改进LeNet-5模型的玉米病害识别[J].江苏农业科学,2022,50(20):35-41.
[24] 张善文,谢泽奇,张晴晴.卷积神经网络在黄瓜叶部病害识别中的应用[J].江苏农业学报,2018,34(1):56-61.
[25] LIANG W J, ZHANG H, ZHANG G F,et al.Rice blast disease recognition using a deep convolutional neural network[J]. Scientific reports,2019,9(2):217-223.
[26] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[EB/OL].https://blog.csdn.net/weixin_36670529/article/details/99976379,2022-09-12.
[27] BRAHIM I, BOUKHAL F A, MOUSSAOU I.Deep learning for tomato diseases: Classification and symptoms visualization[J]. Applied artificial intelligence, 2017, 31(4): 299-315.
[28] 杨国国,鲍一丹,刘子毅.基于图像显著性分析与卷积神经网络的茶园害虫定位与识别[J]. 农业工程学报,2017,33(6):156-162.
[29] 刘婷婷,王婷,胡林.基于卷积神经网络的水稻纹枯病图像识别[J].中国水稻科学,2019,33(1):90-94.
[30] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[EB/OL].https://blog.csdn.net/weixin_36670529/article/details/102961210,2022-06-16.
[31] 鲍文霞,吴刚,胡根生,等.基于改进卷积神经网络的苹果叶部病害识别[J].安徽大学学报(自然科学版),2021,45(1):53-59.
[32] 周云成,许童羽,郑伟,等.基于深度卷积神经网络的番茄主要器官分类识别方法[J].农业工程学报,2017,33(15):219-226.
[33] HE K M, ZHANG X Y, REN S Q, et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE transactions on pattern analysis and machine intelligence, 2014,37(9):1904-1916.
[34] 计雪伟,霍兴赢,薛端,等.基于深度学习的农作物病虫害识别方法[J].南方农机,2020,51(23):182-183.
[35] 王春山,周冀,吴华瑞,等.改进Multi-scale ResNet的蔬菜叶部病害识别[J].农业工程学报,2020,36(20):209-217.
[36] MARRIAM N, TAHIRA N, ALI J, et al.A robust deep learning approach for tomato plant leaf disease localization and classification[J]. Scientific reports,2022,12(1):1-18.
[37] CAO F K,BAI T,XU X L.Vehicle detection and classification based on highway monitoring video[J].Computer systems & applications,2020,29(10):267-273.
[38] MOHANTY S P, HUGHES D P, SALATHE M.Using deep learning for image-based plant disease detection[J]. Frontiers in plant science,2016,7:1419-1437.
[39] ZHANG X H, QIAO Y, MENG F F, et al.Identification of maize leaf diseases using improved deep convolutional neural networks[J]. IEEE sccess,2018,6:30370-30377.
[40] ASHRAFUL H, SUDEEP M, CHANDAN K D, et al.Deep learning-based approach for identification of diseases of maize crop[J]. Scientific reports,2022,12:10140.
[41] HOWARD A G,ZHU M,CHEN B,et al. MobileNets:Efficient convolutional neural networks for mobile vision applications[J/OL].https://blog.csdn.net/qq_41200212/article/details/129598358,2023-04-11.
[42] SANDLER M, HOWARD A, ZHU M, et al. MobileNet V2: Inverted residuals and linear bottlenecks[EB/OL].https://blog.csdn.net/qq_40243750/article/details/126651115,2022-09-01.
[43] 王哲豪,范丽丽,何前.基于MobileNet V2和迁移学习的番茄病害识别[J].江苏农业科学,2023,51(9):215-221.
[44] 孙俊,朱伟栋,罗元秋,等.基于改进MobileNet-V2 的田间农作物叶片病害识别[J].农业工程学报,2021,37(22):161-169.

基于深度学习的农作物病虫害识别研究进展

Research progress in crop disease and pest identification based on deep learning

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价

[1]	袁鹏, 王珂, 肖坚. 基于RMAU-Net网络模型的高分影像耕地提取[J]. 湖北农业科学, 2023, 62(8): 182-188.
[2]	邱儒琼, 彭少坤, 李孟璠. 基于深度学习和遥感数据的水稻种植面积提取[J]. 湖北农业科学, 2023, 62(11): 176-182.
[3]	刘震, 纪明妹, 郭志顶, 黄素芳, 赵忠祥, 闫旭东, 滕霄, 石秘, 岳明强, 刘青松, 徐玉鹏. 河北省农业害虫图像识别系统建设[J]. 湖北农业科学, 2022, 61(7): 135-139.
[4]	李辉, 罗敏, 岳佳欣. 基于计算机视觉技术的水稻病害图像识别研究进展[J]. 湖北农业科学, 2022, 61(4): 9-15.
[5]	雷朦, 余顺园. 基于卷积神经网络的魔芋病害识别[J]. 湖北农业科学, 2022, 61(23): 197-201.
[6]	孙吉红, 钱晔, 周正, 张剑波. 深度学习环境下香料种植推荐模型的设计研究[J]. 湖北农业科学, 2022, 61(22): 174-177.
[7]	王培福, 孙一丹, 鹿子涵, 王伟, 陈晓峰. 基于深度学习的板栗分级方法研究[J]. 湖北农业科学, 2022, 61(21): 168-175.
[8]	陈行, 夏丽华, 颜军, 蒋晓旭, 黄腾杰, 邓剑文. 基于改进U-Net网络模型的高分影像养殖池塘提取[J]. 湖北农业科学, 2022, 61(20): 166-171.
[9]	罗宇, 罗林艳, 范嘉智, 段思汝, 高文娟. 基于深度GRU神经网络的逐小时气温预报模型[J]. 湖北农业科学, 2021, 60(6): 119-122.