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Research of Image Recognition of Plant Diseases and Pests Based on Deep Learning

Research of Image Recognition of Plant Diseases and Pests Based on Deep Learning

Wang Ke Feng, Huang Xue Hua
Copyright: © 2021 |Volume: 15 |Issue: 4 |Pages: 21
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859857|DOI: 10.4018/IJCINI.295810
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MLA

Feng, Wang Ke, and Huang Xue Hua. "Research of Image Recognition of Plant Diseases and Pests Based on Deep Learning." IJCINI vol.15, no.4 2021: pp.1-21. http://doi.org/10.4018/IJCINI.295810

APA

Feng, W. K. & Xue Hua, H. (2021). Research of Image Recognition of Plant Diseases and Pests Based on Deep Learning. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4), 1-21. http://doi.org/10.4018/IJCINI.295810

Chicago

Feng, Wang Ke, and Huang Xue Hua. "Research of Image Recognition of Plant Diseases and Pests Based on Deep Learning," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.4: 1-21. http://doi.org/10.4018/IJCINI.295810

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Abstract

Deep learning has attracted more and more attention in speech recognition, visual recognition and other fields. In the field of image processing, using deep learning method can obtain high recognition rate. In this paper, the convolution neural network is used as the basic model of deep learning. The shortcomings of the model are analyzed, and the DBN is used for the image recognition of diseases and insect pests. In the experiment, firstly, we select 10 kinds of disease and pest leaves and 50000 normal leaves, each of which is used for the comparison of algorithm performance.In the judgment of disease and pest species, the algorithm proposed in this study can identify all kinds of diseases and insect pests to the maximum extent, but the corresponding software (openCV, Access) recognition accuracy will gradually reduce along with the increase of the types of diseases and insect pests. In this study, the algorithm proposed in the identification of diseases and insect pests has been kept at about 45%.