Intelligent Plant Disease Diagnosis with Explainable AI Methods and Lightweight Model

Ishan Joshi, Naman Mardia, R. Vidhya

2025

Abstract

Agriculture is a most important contributor to a country economy and especially in India, as majority of rural people, its only source of livelihood. Plant diseases are among the most significant challenges to agriculture, which can be caused by pathogen, synthetic fertilizers, outdated farming practices, and environmental conditions. The yield for crops can be greatly reduced by these diseases that lead to substantial coronavirus economic impact. AI and Machine Learning techniques for outbreak detection have become widely used by researchers to tackle this issue. This survey examines prevalent plant leaf diseases, explores traditional and deep learning methods for disease detection, and reviews available datasets. It also addresses the use of Explainable AI (XAI) applied to deep learning to enhance the transparency of the models, leading to understandable decisions for the user. Drawing on this expertise, the survey provides insights for researchers, practitioners, and other stakeholders, informative the creation of effective and transparent biosolutions to plant diseases, resulting in sustainable agricultural systems.

Paper Citation

in Harvard Style

Joshi I., Mardia N. and Vidhya R. (2025). Intelligent Plant Disease Diagnosis with Explainable AI Methods and Lightweight Model. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 739-744. DOI: 10.5220/0013904700004919

in Bibtex Style

@conference{icrdicct`2525,
author={Ishan Joshi and Naman Mardia and R. Vidhya},
title={Intelligent Plant Disease Diagnosis with Explainable AI Methods and Lightweight Model},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={739-744},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013904700004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Intelligent Plant Disease Diagnosis with Explainable AI Methods and Lightweight Model
SN - 978-989-758-777-1
AU - Joshi I.
AU - Mardia N.
AU - Vidhya R.
PY - 2025
SP - 739
EP - 744
DO - 10.5220/0013904700004919
PB - SciTePress