Snake Gourd Leaf Disease Detection Using Deep Learning

Abstract

This study explores a novel approach to identifying diseases in snake gourd leaves using advanced deep-learning techniques. The research focuses on five specific leaf conditions: Healthy, Powdery Mildew, Downy Mildew, Yellow, and Anthracnose. A custom dataset of leaf images, normalized to 224x224 pixels, forms the foundation of the study. Preprocessing techniques such as contrast stretching and gamma correction are employed to enhance image quality, ensuring robust inputs for the models. The study evaluates several cutting-edge deep learning architectures, including VGG19, MobileNetV2, and ResNet50V2, for classifying the leaf conditions. Among these, VGG19 emerges as the most promising model, achieving an impressive accuracy of 91.35%. This demonstrates the model’s potential for reliable disease detection in real-world applications. The proposed solution automates the disease detection process, offering a practical and scalable tool for early diagnosis in snake gourd cultivation. By enabling farmers to identify diseases at an early stage, this system helps prevent crop loss and improves agricultural productivity. The integration of artificial intelligence into precision agriculture, as demonstrated in this study, highlights its transformative potential in addressing challenges faced by modern farming. Furthermore, the research lays a solid foundation for future advancements in plant disease detection systems, offering insights into the development of more effective and accessible tools for agricultural applications. With its focus on leveraging state-of-the-art technology, this work contributes significantly to the growing field of AI-driven solutions in sustainable farming practices, ensuring better yields and enhanced food security.