Plant Disease Identification and Pesticides Recommendations Using CNN Deep Learning

Jujaray Vyshnavi, Sangapatnam Sowmya, P. Dhanvitha, Mandozai Ayesha Khatoon, L. Sandhya Rekha

2025

Abstract

Diseases on plants are a major threat to global agricultural productivity and cause great economic losses and problems of food security. Current disease detection methods employ manual inspection, which is slow, mistakes prone and aims to disease specific knowledge. Therefore, to mitigate these challenges, there is need to develop an AI driven Plant disease identification and pesticide recommendation system using convolutional neural networks (CNNs). Using deep learning techniques, the leaf images are automatically classified to generate plant disease to a very high accuracy. Thereafter, it offers real-time and disease dependent pesticide recommendations, maximizing treatment efficiency while reducing pesticides not needed. The model is trained on the available diseased and healthy plants images after a few preprocessing operations, then feature extraction and classification using CNN architecture. One main benefit of this method is the availability for real time disease diagnosis, the decreased dependence of agricultural experts, increased crop yield and usage of environmentally sustainable pesticides. The system is web or mobile application deployable so it can be distributed to the farmers. Also, we can further improve the predictive accuracy by tracking environmental condition like temperature, humidity, and soil health through integrating IoT. The purpose of this research is to provide an automated, scalable, and cost-effective solution to the problem of plant disease management in order to enhance precision agriculture. Although this existing system is useful for the future, it may be further improved this way: multi disease detection, disease affected zone localization, and cloud-based updating for continuous learning.

Paper Citation

in Harvard Style

Vyshnavi J., Sowmya S., Dhanvitha P., Khatoon M. and Rekha L. (2025). Plant Disease Identification and Pesticides Recommendations Using CNN Deep Learning. 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 153-160. DOI: 10.5220/0013909500004919

in Bibtex Style

@conference{icrdicct`2525,
author={Jujaray Vyshnavi and Sangapatnam Sowmya and P. Dhanvitha and Mandozai Khatoon and L. Rekha},
title={Plant Disease Identification and Pesticides Recommendations Using CNN Deep Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={153-160},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013909500004919},
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 - Plant Disease Identification and Pesticides Recommendations Using CNN Deep Learning
SN - 978-989-758-777-1
AU - Vyshnavi J.
AU - Sowmya S.
AU - Dhanvitha P.
AU - Khatoon M.
AU - Rekha L.
PY - 2025
SP - 153
EP - 160
DO - 10.5220/0013909500004919
PB - SciTePress