ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
AI-Powered Detection of Fungal Diseases Using Machine Learning and Computer Vision Techniques
Provisionally accepted
- 1Northeast Forestry University, Harbin, China
- 2Ajman University, Ajman, United Arab Emirates
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
This study evaluates the effectiveness of AI-based machine learning models in the early detection of fungal diseases in tomato crops, both in greenhouses and in open fields. Approximately 20,000 annotated images of two tomato varieties were acquired using high-resolution multispectral and microclimate sensors. Five machine learning models were compared, with convolutional neural networks demonstrating the greatest performance. These networks achieved 95.2% accuracy in greenhouses and 92.5% in open fields, as well as the most favourable area under the curve (AUC) values. Machine learning consistently outperforms traditional diagnostic methods in terms of accuracy, speed of diagnosis, and early detection, thereby reducing the need for fungicides. These results emphasise the potential of integrating AI systems into precision agriculture to enhance crop protection and improve the efficiency of resource use.
Keywords: AI-based disease diagnosis, deep learning, Diagnostic accuracy, Fungal diseases, Greenhouses, machine learning, smart agriculture, tomatoes
Received: 15 Sep 2025; Accepted: 22 Dec 2025.
Copyright: © 2025 Jiang, Al Said and Yin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaowei Yin
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.