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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 | |
| 作者姓名： | 邵明月 张建华 冯全 柴秀娟 张凝 张文蓉 |
| 作者单位： | 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 甘肃农业大学 机电工程学院，甘肃 兰州 730070 |
| 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,... |
| 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 |
| 收稿时间： | 2021-09-30 |
Research Progress of Deep Learning in Detection and Recognition of Plant Leaf Diseases |
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| Authors: | SHAO Mingyue ZHANG Jianhua FENG Quan CHAI Xiujuan ZHANG Ning ZHANG Wenrong |
| Institution: | Agricultural Information Institute of Chinese Academy of Agricultural Sciences/Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 10081, China School of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China |
| 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. |
| Keywords: | plants leaf disease deep learning disease detection recognition convolutional neural network plant diseases image data set |
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