AAAI 2025technical0 citations
Utilizing Vision-Language Models for Detection of Leaf-Based Diseases in Tomatoes
Abstract
Leaf based diseases in tomatoes such as early blight, late blight, and septoria leaf spot, pose a significant threat to global food security and have substantial economic impacts. Early detection of these diseases is crucial for improving crop yields. This paper explores the use of vision-language models (VLMs) for detecting tomato leaf diseases by fine-tuning a pre-trained model on a large dataset of tomato leaf images with corresponding disease annotations. This approach enhances disease detection accuracy and enables multi-modal learning, real-time monitoring, and automated diagnosis, offering promising applications in precision farming and food production.
BibTeX
@article{Blossom Eleojo_2025, title={Utilizing Vision-Language Models for Detection of Leaf-Based Diseases in Tomatoes}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35327}, DOI={10.1609/aaai.v39i28.35327}, abstractNote={Leaf based diseases in tomatoes such as early blight, late blight, and septoria leaf spot, pose a significant threat to global food security and have substantial economic impacts. Early detection of these diseases is crucial for improving crop yields. This paper explores the use of vision-language models (VLMs) for detecting tomato leaf diseases by fine-tuning a pre-trained model on a large dataset of tomato leaf images with corresponding disease annotations. This approach enhances disease detection accuracy and enables multi-modal learning, real-time monitoring, and automated diagnosis, offering promising applications in precision farming and food production.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Blossom Eleojo, James}, year={2025}, month={Apr.}, pages={29567-29569} }