Research on high-order residual convolution neural network for crop disease recognition application

doi:10.3969/j.issn.0253-2778.2019.10.002

Abstract

Abstract: Current research works focusing on the image recognition of crop disease in simple background have achieved great success. However, when handling the problem of crop disease recognition with various noise and complex backgrounds, it is difficult to meet the requirement of recognition accuracy. To address these issues, a new high-order residual convolution neural network for crop disease recognition is proposed, which can realize crop disease recognition that is both accurate and anti-interference. Extensive experimental results demonstrate that the proposed method has high accuracy, strong robustness as well as good anti-interference ability, and can better meet the practical application requirements for crop disease recognition.

Key words: crop disease recognition, high-order residual(HOR), robustness, convolutional neural network (CNN)

CLC Number:

TP391

ZENG Weihui, LI Miao, ZHANG Jian, HUANG Xiaoping, WANG Jingxian, YUAN Yuan. Research on high-order residual convolution neural network for crop disease recognition application[J]. Journal of University of Science and Technology of China, 2019, 49(10): 781-790.

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