Analysis of Crop Pest and Disease Detection Methods Based on Deep Learning

DOI

10.2991/978-94-6463-910-0_3How to use a DOI?

Keywords

Crops; Pest Detection; Disease Detection; Deep Learning

Abstract

Crop pests and diseases are important factors affecting agricultural production. Traditional manual detection methods are inefficient and inaccurate. With the development of artificial intelligence technology, deep learning methods provide new solutions for pest and disease detection. This paper reviews the detection methods of crop pests and diseases based on deep learning, including a detailed analysis of the application of various advanced models and technologies. The specific research methods of this paper include sorting out and assessing the application of deep learning models such as Faster R-CNN and YOLO, and discussing the performance and improvement schemes of these models in disease and pest detection. The research results show that these deep learning models have significant advantages in feature extraction and detection accuracy, but there are still challenges in dealing with complex environments and high-density pest detection. This paper provides reference and inspiration for the research in this field by analyzing and summarizing the related research on crop pests and diseases based on deep learning, which is conducive to promoting the intelligent and sustainable development of agricultural production.

Copyright

© 2025 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.

Download article (PDF)

TY  - CONF
AU  - Hong Pok Ma
PY  - 2025
DA  - 2025/12/15
TI  - Analysis of Crop Pest and Disease Detection Methods Based on Deep Learning
BT  - Proceedings of the 2025 2nd International Symposium on Agricultural Engineering and Biology (ISAEB 2025)
PB  - Atlantis Press
SP  - 13
EP  - 20
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-910-0_3
DO  - 10.2991/978-94-6463-910-0_3
ID  - Ma2025
ER  -