Plant Disease Detection Using Machine Learning

Abstract

Agriculture plays a crucial role in sustaining societies worldwide, and effective crop management is paramount for food security. Plant diseases pose significant threats to agricultural productivity, making timely detection imperative. Leveraging advancements in deep learning and machine vision, this research explores the application of Convolutional Neural Networks (CNNs) to detect tomato leaf diseases. A novel dataset comprising images of diseased and healthy tomato leaves is introduced and utilized for model training. By employing the Inception V3 architecture and data augmentation techniques, the proposed CNN model demonstrates promising results in disease classification. This study presents a framework for leveraging AI/ML techniques to enhance plant disease diagnosis and contribute to global food security efforts. Keywords: Plant Disease Detection, Deep Learning, Convolutional Neural Networks, Inception V3, Agriculture, Tomato Leaf Diseases.