期刊
NEUROCOMPUTING
卷 267, 期 -, 页码 378-384出版社
ELSEVIER
DOI: 10.1016/j.neucom.2017.06.023
关键词
Identification of rice diseases; Convolutional neural networks; Deep learning; Image recognition
资金
- National Natural Science Foundation of China [61374127, 61422301]
- Outstanding Youth Science Foundation of Heilongjiang Province [JC2015016]
- Natural Science Foundation of Heilongjiang Province [F201428]
- Science and Technology Research of Agricultural Bureau in Heilongjiang Province [HNK125B-04-03]
- China Postdoctoral Science Foundation [2016M591560]
- Heilongjiang Postdoctoral Financial Assistance [LBH-Z15185]
- Heilongjiang Bayi Agricultural University Foundation [XA2016-05]
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Opening Fund [MJUKF201729]
The automatic identification and diagnosis of rice diseases are highly desired in the field of agricultural information. Deep learning is a hot research topic in pattern recognition and machine learning at present, it can effectively solve these problems in vegetable pathology. In this study, we propose a novel rice diseases identification method based on deep convolutional neural networks (CNNs) techniques. Using a dataset of 500 natural images of diseased and healthy rice leaves and stems captured from rice experimental field, CNNs are trained to identify 10 common rice diseases. Under the 10-fold cross-validation strategy, the proposed CNNs-based model achieves an accuracy of 95.48%. This accuracy is much higher than conventional machine learning model. The simulation results for the identification of rice diseases show the feasibility and effectiveness of the proposed method. (C) 2017 Elsevier B.V. All rights reserved.
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