Automated Plant Health Diagnosis using Deep CNN Models

Abstract

Plant diseases pose a major threat to sustainable agriculture by decreasing yields and leading to considerable economic setbacks. Traditional detection methods are often manual, time- consuming, and reliant on expert evaluation, which limits their scalability. The model was trained and tested on a diverse dataset containing labeled images of both healthy and infected plant leaves. The findings suggest that real-time disease monitoring using deep learning has the potential to transform precision agriculture by providing efficient and scalable solutions for early disease detection.