Leafscan Ai: Deep Learning-Based Plant Leaf Disease Detection System

Authors: Kartikkey Uttam Khot, Srikant Rajkumar Kadam, Prathmesh Vijay Pawa, Pranit Chandrakant Gaikwad

Abstract: Plant diseases are one of the leading causes of agricultural productivity loss worldwide, threatening food security and farmer livelihoods. Traditional disease identification methods rely on manual visual inspection by agricultural experts, a process that is slow, expensive, and prone to error. This paper presents LeafScan AI, a deep learning-based plant leaf disease detection system capable of identifying 33 distinct disease categories across 9 plant species — Apple, Cherry, Corn, Grape, Peach, Pepper, Potato, Strawberry, and Tomato — with up to 96.88% classification accuracy. The system employs a Convolutional Neural Network (CNN) trained on the publicly available PlantVillage dataset, integrated with a Flask web application for real-time prediction via image upload. OpenCV is used for image preprocessing, and TensorFlow/Keras provides the deep learning backbone. The proposed system enables farmers and agricultural professionals to obtain instant, accurate disease diagnoses from leaf photographs, facilitating timely intervention and minimizing crop losses. Experimental results demonstrate high accuracy across diverse disease categories, validating the practical deployability of the model for smart agriculture applications.

DOI: https://doi.org/10.5281/zenodo.20302936