Application of Computer Vision and Machine Learning to Recognition of Rice Leaf Diseases

DOI

10.2991/978-94-6463-540-9_81How to use a DOI?

Keywords

Application; Computer Vision; Machine Learning; Automatic Recognition; Rice Leaf Diseases

Abstract

As global population growth poses an increasing challenge to agriculture, the importance of crop pest management has increased. At present, most pest problems are solved by traditional manual methods, which are becoming increasingly inefficient in the face of increasing production capacity, so automated pest management has begun to attract people’s attention. This study compared the performance of traditional models and advanced models in several fields of artificial intelligence in disease recognition tasks. The results show that Convolutional Neural Network (CNN) model has the best performance in recognition accuracy, but the execution efficiency is low. XGBoost model has an advantage in processing speed. Support vector machine (SVM) models do not perform well in identifying specific disease classes. The Random forest (RF) model also performs poorly. These experimental results show the potential and limitations of different technologies in improving the efficiency of crop disease management.

Copyright

© 2024 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  - Pengshao Ye
PY  - 2024
DA  - 2024/10/16
TI  - Application of Computer Vision and Machine Learning to Recognition of Rice Leaf Diseases
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 802
EP  - 815
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_81
DO  - 10.2991/978-94-6463-540-9_81
ID  - Ye2024
ER  -