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A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

Xiaozhong Yu1,2,*, Jinhua Zheng1,2

1 College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China
2 Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, 421002, China

* Corresponding Author: Xiaozhong Yu. Email: email

Computers, Materials & Continua 2024, 78(1), 197-225.


In rice production, the prevention and management of pests and diseases have always received special attention. Traditional methods require human experts, which is costly and time-consuming. Due to the complexity of the structure of rice diseases and pests, quickly and reliably recognizing and locating them is difficult. Recently, deep learning technology has been employed to detect and identify rice diseases and pests. This paper introduces common publicly available datasets; summarizes the applications on rice diseases and pests from the aspects of image recognition, object detection, image segmentation, attention mechanism, and few-shot learning methods according to the network structure differences; and compares the performances of existing studies. Finally, the current issues and challenges are explored from the perspective of data acquisition, data processing, and application, providing possible solutions and suggestions. This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.


Cite This Article

X. Yu and J. Zheng, "A review on the application of deep learning methods in detection and identification of rice diseases and pests," Computers, Materials & Continua, vol. 78, no.1, pp. 197–225, 2024.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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