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.
Cite
Text
Eleojo. "Utilizing Vision-Language Models for Detection of Leaf-Based Diseases in Tomatoes." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35327Markdown
[Eleojo. "Utilizing Vision-Language Models for Detection of Leaf-Based Diseases in Tomatoes." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/eleojo2025aaai-utilizing/) doi:10.1609/AAAI.V39I28.35327BibTeX
@inproceedings{eleojo2025aaai-utilizing,
title = {{Utilizing Vision-Language Models for Detection of Leaf-Based Diseases in Tomatoes}},
author = {Eleojo, James Blossom},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {29567-29569},
doi = {10.1609/AAAI.V39I28.35327},
url = {https://mlanthology.org/aaai/2025/eleojo2025aaai-utilizing/}
}