Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach

doi:10.1109/TIP.2021.3049334

	Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach
	Liu, Xinda 1,2; Min, Weiqing 3,4; Mei, Shuhuan 5; Wang, Lili 1,2; Jiang, Shuqiang 3,4
	2021
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	30 页码:2003-2015
摘要	Plant disease diagnosis is very critical for agriculture due to its importance for increasing crop production. Recent advances in image processing offer us a new way to solve this issue via visual plant disease analysis. However, there are few works in this area, not to mention systematic researches. In this paper, we systematically investigate the problem of visual plant disease recognition for plant disease diagnosis. Compared with other types of images, plant disease images generally exhibit randomly distributed lesions, diverse symptoms and complex backgrounds, and thus are hard to capture discriminative information. To facilitate the plant disease recognition research, we construct a new large-scale plant disease dataset with 271 plant disease categories and 220,592 images. Based on this dataset, we tackle plant disease recognition via reweighting both visual regions and loss to emphasize diseased parts. We first compute the weights of all the divided patches from each image based on the cluster distribution of these patches to indicate the discriminative level of each patch. Then we allocate the weight to each loss for each patch-label pair during weakly-supervised training to enable discriminative disease part learning. We finally extract patch features from the network trained with loss reweighting, and utilize the LSTM network to encode the weighed patch feature sequence into a comprehensive feature representation. Extensive evaluations on this dataset and another public dataset demonstrate the advantage of the proposed method. We expect this research will further the agenda of plant disease recognition in the community of image processing.
关键词	Diseases Agriculture Plants (biology) Visualization Image recognition Feature extraction Medical diagnosis Plant disease recognition fine-grained visual classification reweighting approach feature aggregation
DOI	10.1109/TIP.2021.3049334
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61932003] ; National Natural Science Foundation of China[61772051] ; National Key Research and Development Plan[2019YFC1521102] ; Beijing Natural Science Foundation[L182016] ; Beijing Program for International S&T Cooperation Project[Z191100001619003] ; Shenzhen Research Institute of Big Data (Shenzhen)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000612145300001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：43[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16315
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Lili
作者单位	1.Beihang Univ, Beijing Adv Innovat Ctr Biomed Engn, State Key Lab Virtual Real Technol & Syst, Beijing 100191, Peoples R China 2.Peng Cheng Lab, Shenzhen 518066, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Beijing Puhui Sannong Technol Co Ltd, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Xinda,Min, Weiqing,Mei, Shuhuan,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.
APA	Liu, Xinda,Min, Weiqing,Mei, Shuhuan,Wang, Lili,&Jiang, Shuqiang.(2021).Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,2003-2015.
MLA	Liu, Xinda,et al."Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):2003-2015.