Guava Disease Detection using Convolutional Neural Networks

Guava is a widely cultivated tropical fruit crop valued for its nutritional richness and economic benefits. However, guava production is often hindered by the prevalence of various leaf diseases, such as anthracnose, rust, and bacterial leaf spot, which significantly affect both yield and fruit quality. Timely and accurate detection of these diseases is critical to prevent widespread damage and ensure better crop management. Traditional disease detection techniques primarily rely on manual observation and expertise, which are not only time-consuming and labor-intensive but also prone to human error. These limitations highlight the need for an automated, efficient, and scalable solution for guava disease identification. This project proposes a deep learning-based approach for the automated detection of guava leaf diseases using Convolutional Neural Networks (CNNs)