Open Access

ARTICLE

Image Recognition of Citrus Diseases Based on Deep Learning

by Zongshuai Liu¹, Xuyu Xiang^1,2,*, Jiaohua Qin¹, Yun Tan¹, Qin Zhang¹, Neal N. Xiong³

1 Central South University of Forestry and Technology, Changsha, 410004, China
2 School of Information Technology and Management, Hunan University of Finance and Economics, Changsha, 410205, China
3 Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK, 74464, USA

* Corresponding Author: Xuyu Xiang. Email:

Computers, Materials & Continua 2021, 66(1), 457-466. https://doi.org/10.32604/cmc.2020.012165

Received 17 June 2020; Accepted 28 July 2020; Issue published 30 October 2020

Abstract

In recent years, with the development of machine learning and deep learning, it is possible to identify and even control crop diseases by using electronic devices instead of manual observation. In this paper, an image recognition method of citrus diseases based on deep learning is proposed. We built a citrus image dataset including six common citrus diseases. The deep learning network is used to train and learn these images, which can effectively identify and classify crop diseases. In the experiment, we use MobileNetV2 model as the primary network and compare it with other network models in the aspect of speed, model size, accuracy. Results show that our method reduces the prediction time consumption and model size while keeping a good classification accuracy. Finally, we discuss the significance of using MobileNetV2 to identify and classify agricultural diseases in mobile terminal, and put forward relevant suggestions.

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Citations