深度学习在植物叶部病害检测与识别的研究进展

doi:10.12133/j.smartag.SA202202005

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (1): 29-46.doi: 10.12133/j.smartag.SA202202005

• 专题--作物生长及其环境监测 • 上一篇下一篇

深度学习在植物叶部病害检测与识别的研究进展

邵明月¹(), 张建华¹(), 冯全², 柴秀娟¹, 张凝¹, 张文蓉¹

^1.中国农业科学院农业信息研究所/农业农村部农业大数据重点实验室，北京 100081
^2.甘肃农业大学机电工程学院，甘肃兰州 730070

收稿日期:2021-09-30 出版日期:2022-03-30
基金资助:
国家自然科学基金(31971792);中央级公益性科研院所基本科研业务费专项(JBYW-AII-2020-07);中国农业科学院基本科研业务费专项(Y2020YJ07);中国农业科学院创新工程(CAAS-ASTIP-2016-AII)
作者简介:邵明月（1997－），女，硕士研究生，研究方向为机器视觉与农业机器人。E-mail：82101205406@caas.cn。
通信作者: 张建华（1982－），男，博士，副研究员，研究方向为机器视觉与农业机器人。E-mail：zhangjianhua@caas.cn

Research Progress of Deep Learning in Detection and Recognition of Plant Leaf Diseases

SHAO Mingyue¹(), ZHANG Jianhua¹(), FENG Quan², CHAI Xiujuan¹, ZHANG Ning¹, ZHANG Wenrong¹

^1.Agricultural Information Institute of Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 10081, China
^2.School of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China

Received:2021-09-30 Online:2022-03-30
corresponding author: ZHANG Jianhua, E-mail：zhangjianhua@caas.cn
About author:SHAO Mingyue, E-mail：82101205406@caas.cn
Supported by:
National Natural Science Foundation of China(31971792);Special Fund for Basic Scientific Research Business of Central-level Public Welfare Scientific Research Institutes(JBYW-AII-2020-07);Basic Research Fund of Chinese Academy of Agricultural Sciences (Y2020YJ07);Chinese Academy of Agricultural Sciences Innovation Project (CAAS-ASTIP-2016-AII)

摘要/Abstract

摘要：

植物病害准确检测与识别是其早期诊断与智能监测的关键，是病虫害精准化防治与信息化管理的核心。深度学习应用于植物病害检测与识别中，可以克服传统诊断方法的弊端，大幅提升病害检测与识别的准确率，引起了广泛关注。本文首先收集和介绍了部分公开的植物病害图像数据集，然后系统地综述了近年来深度学习在植物病害检测和识别中的研究应用进展，阐述了从早期检测和识别算法到基于深度学习的检测和识别算法的研究进展，以及各算法的优点和存在的问题。调研了相关研究文献，提出了光照、遮挡、复杂背景、病害症状之间相似性、病害在不同时期症状会有不同的变化以及多种病害交叠共存是目前植物病害检测和识别面临的主要挑战。并进一步指出，将性能更好的神经网络、大规模数据集和农业理论基础相结合，是未来主要的发展趋势，同时还指出了多模态数据可以用于植物早期病害的识别，也是未来发展方向之一。本文可为植物病害识别的深入研究与发展提供参考。

关键词: 植物, 叶部病害, 深度学习, 病害检测, 识别, 卷积神经网络, 病害图像数据集

Abstract:

Accurate detection and recognition of plant diseases is the key technology to early diagnosis and intelligent monitoring of plant diseases, and is the core of accurate control and information management of plant diseases and insect pests. Deep learning can overcome the disadvantages of traditional diagnosis methods and greatly improve the accuracy of diseases detection and recognition, and has attracted a lot of attention of researchers. This paper collected the main public plant diseases image data sets all over the world, and briefly introduced the basic information of each data set and their websites, which is convenient to download and use. And then, the application of deep learning in plant disease detection and recognition in recent years was systematically reviewed. Plant disease target detection is the premise of accurate classification and recognition of plant disease and evaluation of disease hazard level. It is also the key to accurately locate plant disease area and guide spray device of plant protection equipment to spray drug on target. Plant disease recognition refers to the processing, analysis and understanding of disease images to identify different kinds of disease objects, which is the main basis for the timely and effective prevention and control of plant diseases. The research progress in early disease detection and recognition algorithm was expounded based on depth of learning research, as well as the advantages and existing problems of various algorithms were described. It can be seen from this review that the detection and recognition algorithm based on deep learning is superior to the traditional detection and recognition algorithm in all aspects. Based on the investigation of research results, it was pointed out that the illumination, sheltering, complex background, different disorders with similar symptoms, different changes of disease symptoms in different periods, and overlapping coexistence of multiple diseases were the main challenges for the detection and recognition of plant diseases. At the same time, the establishment of a large-scale and more complex data set that meets the specific research needs is also a difficulty that need to face together. And at further, we point out that the combination of the better performance of the neural network, large-scale data set and agriculture theoretical basis is a major trend of the development of the future. It is also pointed out that multimodal data can be used to identify early plant diseases, which is also one of the future development direction.

Key words: plants, leaf disease, deep learning, disease detection, recognition, convolutional neural network, plant diseases image data set

中图分类号:

S432

邵明月, 张建华, 冯全, 柴秀娟, 张凝, 张文蓉. 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.

SHAO Mingyue, ZHANG Jianhua, FENG Quan, CHAI Xiujuan, ZHANG Ning, ZHANG Wenrong. Research Progress of Deep Learning in Detection and Recognition of Plant Leaf Diseases[J]. Smart Agriculture, 2022, 4(1): 29-46.

参考文献

1	NAVROZIDIS I, ALEXANDRIDIS T K, DIMITRAKOS A, et al. Identification of purple spot disease on asparagus crops across spatial and spectral scales[J]. Computers and Electronics in Agriculture, 2018, 148: 322-329.
2	MA J, DU K, ZHENG F, et al. A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network[J]. Computers and Electronics in Agriculture, 2018, 154: 18-24.
3	CHEN P, XIAO Q, ZHANG J, et al. Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation[J]. Computers and Electronics in Agriculture, 2020, 176: ID 105612.
4	张建华, 韩书庆, 翟治芬, 等. 改进自适应分水岭方法分割棉花叶部粘连病斑[J]. 农业工程学报, 2018, 34(24): 165-174.
4	ZHANG J, HAN S, ZHAI Z, et al. Segmentation of cotton leaf adhesiosis spot by improved adaptive watershed method [J]. Transactions of the CSAE, 2018, 34(24): 165-174.
5	VISHNOI V K, KUMAR K, KUMAR B. Plant disease detection using computational intelligence and image processing[J]. Journal of Plant Diseases and Protection, 2021, 128(1): 19-53.
6	高秀美, 曹长余, 邵增顺, 等. 园林植物病虫害发生特点与防治对策[J]. 中国农学通报, 2001, 17(1): 70-71.
6	GAO X, CAO C, SHAO Z, et al. Occurrence characteristics and control countermeasures of garden plant diseases and insect pests[J]. Chinese Agricultural Science Bulletin, 2001, 17(1): 70-71.
7	刘媛. 基于深度学习的葡萄叶片病害识别方法研究[D]. 兰州: 甘肃农业大学, 2018.
7	LIU Y. Research on grape leaf disease identification method based on deep learning[D]. Lanzhou: Gansu Agricultural University, 2018.
8	ZHANG J, KONG F, WU J, et al. Automatic image segmentation method for cotton leaves with disease under natural environment[J]. Journal of Integrative Agriculture 2018, 17(8): 1800-1814.
9	ZHANG J, KONG F, ZHAI Z, et al. Robust image segmentation method for cotton leaf under natural conditions based on immune algorithm and PCNN algorithm[J]. International Journal of Pattern Recognition & Artificial Intelligence, 2018, 32(5): 3671-3678.
10	HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554.
11	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[J]. Advances in Neural Information processing systems, 2012, 25: 1097-1105.
12	DARGAN S, KUMAR M, AYYAGARI M R, et al. A survey of deep learning and its applications: A new paradigm to machine learning[J]. Archives of Computational Methods in Engineering, 2020, 27(4): 1071-1092.
13	ZHANG Q, LIU Y, GONG C, et al. Applications of deep learning for dense scenes analysis in agriculture: A review[J]. Sensors, 2020, 20(5): ID 1520.
14	SANTOS L, SANTOS F N, OLIVEIRA P M, et al. Deep learning applications in agriculture: A short review[C]// Iberian Robotics Conference. Berlin,German:Springer, Cham, 2019: 139-151.
15	BARBEDO J G A. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification[J]. Computers and Electronics in Agriculture, 2018, 153: 46-53.
16	SINGH D, JAIN N, JAIN P, et al. PlantDoc: A dataset for visual plant disease detection[C]// The 7th ACM IKDD CoDS and 25th COMAD. New York, USA: ACM, 2020: 249-253.
17	LIU X, MIN W, MEI S, et al. Plant disease recognition: A large-scale benchmark dataset and a visual region and loss reweighting approach[J]. IEEE Transactions on Image Processing, 2021, 30: 2003-2015.
18	BARBEDO J G A, KOENIGKAN L V, HALFELD-VIEIRA B A, et al. Annotated plant pathology databases for image-based detection and recognition of diseases[J]. IEEE Latin America Transactions, 2018, 16(6): 1749-1757.
19	张文静. 基于卷积神经网络的烟草病害识别与检测[D]. 泰安: 山东农业大学, 2021.
19	ZHANG W. Tobacco disease recognition and detection based on convolutional neural network[J]. Tai'an: Shandong Agricultural University, 2021.
20	LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521: 436-444.
21	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// The IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, New York, USA: IEEE, 2014: 580-587.
22	GIRSHICK R. Fast r-cnn[C]// The IEEE International Conference on Computer Vision. Piscataway, New York, USA: IEEE, 2015: 1440-1448.
23	REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. Advances in Neural Information Processing Systems, 2015, 28: 91-99.
24	FUENTES A, YOON S, KIM S C, et al. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition[J]. Sensors, 2017, 17(9): ID 2022.
25	刘阗宇, 冯全. 基于卷积神经网络的葡萄叶片检测[J]. 西北大学学报(自然科学版), 2017, 47(4): 505-512.
25	LIU T, FENG Q. Grape leaf detection based on convolutional neural network[J]. Journal of Northwest University (Natural Science Edition), 2017, 47(4): 505-512.
26	刘阗宇, 冯全, 杨森. 基于卷积神经网络的葡萄叶片病害检测方法[J]. 东北农业大学学报, 2018, 49(3): 73-83.
26	LIU T, FENG Q, YANG S. Detection of grape leaf diseases based on convolutional neural network[J]. Journal of Northeast Agricultural University, 2018, 49(3): 73-83.
27	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: ID 122537.
28	BARI B S, ISLAM M N, RASHID M, et al. A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework[J]. PeerJ Computer Science, 2021, 7: ID e432.
29	ZHOU G, ZHANG W, CHEN A, et al. Rapid detection of rice disease based on FCM-KM and faster R-CNN fusion[J]. IEEE Access, 2019, 7: 143190-143206.
30	XIE X, MA Y, LIU B, et al. A deep-learning-based real-time detector for grape leaf diseases using improved convolutional neural networks[J]. Frontiers in Plant Science, 2020, 11: ID 751.
31	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]// The IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, New York, USA: IEEE, 2016: 779-788.
32	BHATT P V, SARANGI S, PAPPULA S. Detection of diseases and pests on images captured in uncontrolled conditions from tea plantations[C]// Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV. International Society for Optics and Photonics. Baltimore, Maryland, United States: SPIE, 2019: ID 1100808.
33	MASKI P, THONDIYATH A. Plant disease detection using advanced deep learning algorithms: A case study of papaya ring spot disease[C]// 2021 6th International Conference on Image, Vision and Computing (ICIVC). Piscataway, New York, USA: IEEE, 2021: 49-54.
34	李昊, 刘海隆, 刘生龙. 基于深度学习的柑橘病虫害动态识别系统研发[J]. 中国农机化学报, 2021, 42(9): 195-201, 208.
34	LI H, LIU H, LIU S. Development of dynamic recognition system of citrus pests and diseases based on deep learning[J]. Journal of Chinese Agricultural Mechanization, 2021, 42(9): 195-201, 208.
35	LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]// European Conference on Computer Vision. Berlin, German: Springer, Cham, 2016: 21-37.
36	SUN H, XU H, LIU B, et al. MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks[J]. Computers and Electronics in Agriculture, 2021, 189: ID 106379.
37	SUN J, YANG Y, HE X, et al. Northern maize leaf blight detection under complex field environment based on deep learning[J]. IEEE Access, 2020, 8: 33679-33688.
38	SELVARAJ M G, VERGARA A, RUIZ H, et al. AI-powered banana diseases and pest detection[J]. Plant Methods, 2019, 15(1): 1-11.
39	ZHOU X, WANG D, KR?HENBüHL P. Objects as points[J/OL]. arXiv preprint arXiv:, 2019.
40	夏雪, 孙琦鑫, 侍啸, 等. 基于轻量级无锚点深度卷积神经网络的树上苹果检测模型[J]. 智慧农业(中英文), 2020, 2(1): 99-110.
40	XIA X, SUN Q, SHI X, et al. Apple detection model based on lightweight anchor-free deep convolutional neural network[J]. Smart Agriculture, 2020, 2(1): 99-110.
41	ALBATTAH W, NAWAZ M, JAVED A, et al. A novel deep learning method for detection and classification of plant diseases[J]. Complex & Intelligent Systems, 2021: 1-18.
42	ZHANG K, WU Q, CHEN Y. Detecting soybean leaf disease from synthetic image using multi-feature fusion faster R-CNN[J]. Computers and Electronics in Agriculture, 2021, 183: ID 106064.
43	HU G, WANG H, ZHANG Y, et al. Detection and severity analysis of tea leaf blight based on deep learning[J]. Computers & Electrical Engineering, 2021, 90: ID 107023.
44	ESER S. A deep learning based approach for the detection of diseases in pepper and potato leaves[J]. Anadolu Journal of Agricultural Sciences, 2021, 36(2): 167-178.
45	REHMAN Z, KHAN M A, AHMED F, et al. Recognizing apple leaf diseases using a novel parallel real‐time processing framework based on MASK RCNN and transfer learning: An application for smart agriculture[J]. IET Image Processing, 2021, 15(10): 2157-2168.
46	ANANDHAN K, SINGH A S. Detection of paddy crops diseases and early diagnosis using faster regional convolutional neural networks[C]// 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). Piscataway, New York, USA: IEEE, 2021: 898-902.
47	KUMAR P. Research paper on sugarcane disease detection model[J]. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(6): 5167-5174.
48	李鑫然, 李书琴, 刘斌. 基于改进Faster R_CNN的苹果叶片病害检测方法[J]. 计算机工程, 2021, 47(11): 298-304.
48	LI X, LI S, LIU B. Detection of apple leaf diseases based on improved Faster R_CNN[J]. Computer Engineering, 2021, 47(11): 298-304.
49	WANG Q, QI F, SUN M, et al. Identification of tomato disease types and detection of infected areas based on deep convolutional neural networks and object detection techniques[J]. Computational Intelligence and Neuroscience, 2019, 2019(2): ID 9142753.
50	乔虹, 冯全, 张芮, 等. 基于时序图像跟踪的葡萄叶片病害动态监测[J]. 农业工程学报, 2018, 34(17): 167-175.
50	QIAO H, FENG Q, ZHANG R, et al. Dynamic monitoring of grape leaf diseases based on sequential image tracking[J]. Transactions of the CSAE, 2018, 34(17): 167-175.
51	WANG J, YANG J, YU L, et al. DBA_SSD: A novel end-to-end object detection using deep attention module for helping smart device with vegetable and fruit leaf plant disease detection[J]. Information, 2021, 12(11): 474.
52	SHILL A, RAHMAN M A. Plant disease detection based on YOLOv3 and YOLOv4[C]// 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI). Piscataway, New York, USA: IEEE, 2021.
53	WANG X, LIU J. Multiscale parallel algorithm for early detection of tomato gray mold in a complex natural environment[J]. Frontiers in Plant Science, 2021, 12: ID 620273.
54	HE X, FANG K, QIAO B, et al. Watermelon disease detection based on deep learning[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2021, 35(5): ID 2152004.
55	ATILA ü, U?AR M, AKYOL K, et al. Plant leaf disease classification using EfficientNet deep learning model[J]. Ecological Informatics, 2021, 61: ID 101182.
56	LIU J, WANG X. Tomato diseases and pests detection based on improved YOLOv3 convolutional neural network[J]. Frontiers in Plant Science, 2020, 11: ID 898.
57	MORBEKAR A, PARIHAR A, JADHAV R. Crop disease detection using YOLO[C]// 2020 International Conference for Emerging Technology (INCET). Piscataway, New York, USA: IEEE, 2020.
58	PONNUSAMY V, COUMARAN A, SHUNMUGAM A S, et al. Smart glass: Real-time leaf disease detection using YOLO transfer learning[C]// 2020 International Conference on Communication and Signal Processing (ICCSP). Piscataway, New York, USA: IEEE, 2020: 1150-1154.
59	LIU J, WANG X. Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model[J]. Plant Methods, 2020, 16: ID 83.
60	JIANG P, CHEN Y, LIU B, et al. Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks[J]. IEEE Access, 2019, 7: 59069-59080.
61	TIAN Y, YANG G, WANG Z, et al. Detection of apple lesions in orchards based on deep learning methods of cyclegan and YOLOv3-dense[J]. Journal of Sensors, 2019: ID 7630926.
62	RAMCHARAN A, MCCLOSKEY P, BARANOWSKI K, et al. A mobile-based deep learning model for cassava disease diagnosis[J]. Frontiers in Plant Science, 2019, 10: ID 272.
63	KAVITHA LAKSHMI R, SAVARIMUTHU N. DPD-DS for plant disease detection based on instance segmentation[J]. Journal of Ambient Intelligence and Humanized Computing, 2021: 1-11.
64	LIU C, ZHU H, GUO W, et al. EFDet: An efficient detection method for cucumber disease under natural complex environments[J]. Computers and Electronics in Agriculture, 2021, 189: ID 106378.
65	DWIVEDI R, DEY S, CHAKRABORTY C, et al. Grape disease detection network based on multi-task learning and attention features[J]. IEEE Sensors Journal, 2021: ID 99.
66	NIHAR F, KHANOM N N, HASSAN S S, et al. Plant disease detection through the implementation of diversified and modified neural network algorithms[J]. Journal of Engineering Advancements, 2021, 2(1): 48-57.
67	NAGASUBRAMANIAN K, JONES S, SINGH A K, et al. Plant disease identification using explainable 3D deep learning on hyperspectral images[J]. Plant Methods, 2019, 15(1): ID 98.
68	KAWASAKI Y, UGA H, KAGIWADA S, et al. Basic study of automated diagnosis of viral plant diseases using convolutional neural networks[C]// International Symposium on Visual Computing. Berlin,German: Springer, Cham, 2015: 638-645.
69	SLADOJEVIC S, ARSENOVIC M, ANDERLA A, et al. Deep neural networks based recognition of plant diseases by leaf image classification[J]. Computational Intelligence and Neuroscience, 2016, 2016: ID 3289801.
70	MOHANTY S P, HUGHES D P, SALATHé M. Using deep learning for image-based plant disease detection[J]. Frontiers in Plant Science, 2016, 7: ID 1419.
71	RAMCHARAN A, BARANOWSKI, MCCLOSKEY P, et al. Deep learning for image-based cassava disease detection[J]. Frontiers in Plant Science, 2017, 23(10): 1-7.
72	孙俊, 谭文军, 毛罕平, 等. 基于改进卷积神经网络的多种植物叶片病害识别[J]. 农业工程学报, 2017, 33(19): 209-215.
72	SUN J, TAN W, MAO H, et al. Recognition of plant leaf diseases based on improved convolutional neural network [J]. Transactions of the CSAE, 2017, 33(19): 209-215.
73	LU J, HU J, ZHAO G, et al. An in-field automatic wheat disease diagnosis system[J]. Computers and Electronics in Agriculture, 2017, 142: 369-379.
74	FERENTINOS K P. Deep learning models for plant disease detection and diagnosis[J]. Computers and Electronics in Agriculture, 2018, 145: 311-318.
75	赵建敏, 李艳, 李琦, 等. 基于卷积神经网络的马铃薯叶片病害识别系统[J]. 江苏农业科学, 2018, 46(24): 251-255.
75	ZHAO J, LI Y, LI Q, et al. Recognition system of potato leaf disease based on convolutional neural network[J]. Jiangsu Agricultural Sciences, 2018, 46(24): 251-255.
76	XING S, LEE M, LEE K K. Citrus pests and diseases recognition model using weakly dense connected convolution network[J]. Sensors, 2019, 19(14): ID 3195.
77	曾伟辉, 李淼, 李增, 等.基于高阶残差和参数共享反馈卷积神经网络的农作物病害识别[J]. 电子学报, 2019, 47(9): 1979-1986.
77	ZENG W, LI M, LI Z, et al. Recognition of crop diseases based on high-order residual and parameter sharing feedback convolutional Neural network[J]. Acta Electronica Sinica, 2019, 47(9): 1979-1986.
78	JI M, ZHANG L, WU Q. Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks[J]. Information Processing in Agriculture, 2020, 7(3): 418-426.
79	刘洋, 冯全, 王书志. 基于轻量级CNN的植物病害识别方法及移动端应用[J]. 农业工程学报, 2019, 35(17): 194-204.
79	LIU Y, FENG Q, WANG S. Plant disease recognition method based on lightweight CNN and its mobile application[J]. Transactions of the CSAE, 2019, 35(17): 194-204.
80	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.
80	WANG C, ZHOU J, WU H, et al. Improved multi-scale ResNet for identification of vegetable leaf diseases[J]. Transactions of the CSAE, 2020, 36(20): 209-217.
81	SALEEM M H, POTGIETER J, ARIF K M. Plant disease classification: A comparative evaluation of convolutional neural networks and deep learning optimizers[J]. Plants, 2020, 9(10): ID 1319.
82	DE OCAMPO A L P, DADIOS E P. Mobile platform implementation of lightweight neural network model for plant disease detection and recognition[C]// 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM). Piscataway, New York, USA: IEEE, 2018: 1-4.
83	DE LUNA R G, DADIOS E P, BANDALA A A. Automated image capturing system for deep learning-based tomato plant leaf disease detection and recognition[C]// TENCON 2018-2018 IEEE Region 10 Conference. Piscataway, New York, USA: IEEE, 2018: 1414-1419.
84	RASHID J, KHAN I, ALI G, et al. Multi-level deep learning model for potato leaf disease recognition[J]. Electronics, 2021, 10(17): ID 2064.
85	KIRATIRATANAPRUK K, TEMNIRANRAT P, KITVIMONRAT A, et al., Using deep learning techniques to detect rice diseases from images of rice fields[C]// International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Berlin, German: Springer, 2020: 225-237.
86	JIANG Z, DONG Z, JIANG W, et al. Recognition of rice leaf diseases and wheat leaf diseases based on multi-task deep transfer learning[J]. Computers and Electronics in Agriculture, 2021, 186: ID 106184.
87	ABBAS A, JAIN S, GOUR M, et al.Tomato plant disease detection using transfer learning with C-GAN synthetic images[J]. Computers and Electronics in Agriculture, 2021, 187: ID 106279.
88	CHELLAPANDI B, VIJAYALAKSHMI M, CHOPRA S. Comparison of pre-trained models using transfer learning for detecting plant disease[C]// 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). Piscataway, New York, USA: IEEE, 2021: 383-387.
89	樊湘鹏, 许燕, 周建平, 等. 基于迁移学习和改进 CNN 的葡萄叶部病害检测系统[J]. 农业工程学报, 2020, 37(6): 151-159.
89	FAN X, XU Y, ZHOU J, et al. Grape leaf disease detection system based on transfer learning and improved CNN[J]. Transactions of the CSAE, 2020, 37(6): 151-159.
90	JIANG F, LU Y, CHEN Y, et al. Image recognition of four rice leaf diseases based on deep learning and support vector machine[J]. Computers and Electronics in Agriculture, 2020, 179: ID 105824.
91	BARMAN U, CHOUDHURY R D, SAHU D, et al. Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease[J]. Computers and Electronics in Agriculture, 2020, 177: ID 105661.
92	DANG L M, HASSAN S I, SUHYEON I, et al. UAV based wilt detection system via convolutional neural networks[J]. Sustainable Computing: Informatics and Systems, 2020, 28: ID 100250.
93	HOWLADER M R, HABIBA U, FAISAL R H, et al. Automatic recognition of guava leaf diseases using deep convolution neural network[C]// 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE). Piscataway, New York, USA: IEEE: 2019.
94	COULIBALY S, KAMSU-FOGUEM B, KAMISSOKO D, et al. Deep neural networks with transfer learning in millet crop images[J]. Computers in Industry, 2019, 108: 115-120.
95	HU G, YANG X, ZHANG Y,et al. Identification of tea leaf diseases by using an improved deep convolutional neural network[J]. Sustainable Computing: Informatics and Systems, 2019, 24: ID 100353.
96	SIBIYA M, SUMBWANYAMBE M. A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks[J]. AgriEngineering, 2019, 1(1): 119-131.
97	王艳玲, 张宏立, 刘庆飞, 等. 基于迁移学习的番茄叶片病害图像分类[J]. 中国农业大学学报, 2019, 24(6): 124-130.
97	WANG Y, ZHANG H, LIU Q, et al. Image classification of tomato leaf diseases based on transfer learning[J]. Journal of China Agricultural University, 2019, 24(6): 124-130.
98	SINGH U P, CHOUHAN S S, JAIN S, et al. Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease[J]. IEEE Access, 2019, 7: 43721-43729.
99	PICON A, ALVAREZ-GILA A, SEITZ M, et al. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild[J]. Computers and Electronics in Agriculture, 2019, 161: 280-290.
100	ABDALLA A, CEN H, WAN L, et al. Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure[J]. Computers and Electronics in Agriculture, 2019, 167: ID 105091.
101	ATOLE R R, PARK D. A multiclass deep convolutional neural network classifier for detection of common rice plant anomalies[J]. International Journal of Advanced Computer Science and Applications, 2018, 9(1): 67-70.
102	ZHANG X, QIAO Y, MENG F, et al. Identification of maize leaf diseases using improved deep convolutional neural networks[J]. IEEE Access, 2018, 6: 30370-30377.
103	LIU B, ZHANG Y, HE D J, et al. Identification of apple leaf diseases based on deep convolutional neural networks[J]. Symmetry, 2018, 10(1): ID 11.
104	张建华, 孔繁涛, 吴建寨, 等. 基于改进VGG卷积神经网络的棉花病害识别模型[J]. 中国农业大学学报, 2018, 23(11): 161-171.
104	ZHANG J, KONG F, WU J, et al. Cotton disease recognition model based on improved VGG convolutional neural network[J]. Journal of China Agricultural University, 2018, 23(11): 161-171.
105	RANGARAJAN A K, PURUSHOTHAMAN R, RAMESH A. Tomato crop disease classification using pre-trained deep learning algorithm[J]. Procedia Computer Science, 2018, 133: 1040-1047.
106	DE CHANT C, WIESNER-HANKS T, CHEN S, et al. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning[J]. Phytopathology, 2017, 107(11): 1426-1432.
107	LU Y, YI S, ZENG N, et al. Identification of rice diseases using deep convolutional neural networks[J]. Neurocomputing, 2017, 267: 378-384.
108	OPPENHEIM D, SHANI G. Potato disease classification using convolution neural networks[J]. Advances in Animal Biosciences, 2017, 8(2): 244-249.
109	FUJITA E, KAWASAKI Y, UGA H, et al. Basic investigation on a robust and practical plant diagnostic system[C]// 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). Piscataway, New York, USA: IEEE, 2016: 989-992.
110	SRINIDHI V V, SAHAY A, DEEBA K. Plant pathology disease detection in apple leaves using deep convolutional neural networks: Apple leaves disease detection using EfficientNet and DenseNet[C]// 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). Piscataway, New York, USA: IEEE, 2021: 1119-1127.
111	ZHOU J, LI J, WANG C, et al. A vegetable disease recognition model for complex background based on region proposal and progressive learning[J]. Computers and Electronics in Agriculture, 2021, 184: ID 106101.
112	ZHANG P, YANG L, LI D. EfficientNet-B4-Ranger: A novel method for greenhouse cucumber disease recognition under natural complex environment[J]. Computers and Electronics in Agriculture, 2020, 176: ID 105652.

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深度学习在植物叶部病害检测与识别的研究进展

Research Progress of Deep Learning in Detection and Recognition of Plant Leaf Diseases

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