Intelligent Detection of Crop Pests and Diseases Based on Deep Learning: Rice Pests and Tomato Leaf Diseases

DOI

10.2991/978-94-6239-648-7_19How to use a DOI?

Keywords

Deep learning; Rice disease detection; Tomato leaf disease detection; YOLO; Convolutional neural network

Abstract

At the critical stage of the development of smart agriculture, integrating mechanical vision and deep learning technologies to achieve precise monitoring of tomato leaf diseases and rapid identification of rice pests is of great significance for enhancing agricultural production efficiency and ensuring food security. However, existing image recognition technologies face numerous challenges, such as difficulties in extracting image features under complex lighting conditions, poor recognition performance in multi-object and multi-scale scenarios, and large computational costs and low recognition accuracy due to blurred boundaries in dynamic images. There is an urgent need to construct intelligent solutions that can adapt to complex agricultural scenarios. This paper focuses on two specific application scenarios: rice pest and disease monitoring and tomato leaf disease monitoring, and evaluates the mainstream object detection models and their improvement schemes in recent years. The research mainly reviews the progress of improved models based on multiple versions of the YOLO series in the past half year. These improved models have effectively enhanced detection performance in complex environments by optimizing network structures (such as introducing lightweight designs and feature fusion mechanisms), providing new algorithmic ideas for addressing the aforementioned challenges.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zexi Li
PY  - 2026
DA  - 2026/04/24
TI  - Intelligent Detection of Crop Pests and Diseases Based on Deep Learning: Rice Pests and Tomato Leaf Diseases
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 164
EP  - 175
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_19
DO  - 10.2991/978-94-6239-648-7_19
ID  - Li2026
ER  -