Plant Leaf Disease Detection and Pesticide Recommendation System using Deep Learning

Abstract

An Agriculture plays a crucial role in the economy, yet crop productivity is significantly affected by plant leaf diseases that often go undetected at early stages. Farmers, especially in rural areas, face challenges in accurately identifying diseases and selecting appropriate pesticides, leading to reduced yield and increased costs. Existing solutions are either manual, timeconsuming, or lack intelligent decision-making capabilities. This paper presents a deep learning-based plant leaf disease detection and pesticide recommendation system designed to address these challenges in an end-to-end manner. The system employs a Convolutional Neural Network (CNN) model trained on a large dataset of plant leaf images to accurately classify diseases across multiple crops. The trained model achieves high accuracy in identifying both healthy and diseased leaves under varied environmental conditions. Once a disease is detected, the system integrates a recommendation module that suggests suitable pesticides and preventive measures based on the identified disease. The complete solution is implemented as a userfriendly web application where users can upload leaf images and receive instant results. The system is designed for real-time usage, ensuring accessibility and ease of use for farmers without requiring technical expertise. By combining computer vision and deep learning with practical agricultural knowledge, this system provides an efficient, scalable, and cost-effective solution for early disease detection and crop management. It has the potential to reduce crop losses, improve productivity, and support sustainable farming practices.

Code and data availability

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