Classification of Guava Leaf Disease using Deep Learning

Abstract

A higher percentage of crops are affected by diseases, posing a challenge to agricultural production. It is possible to increase productivity by detecting and forecasting diseases early. Guava is a fruit grown in tropical and subtropical countries such as Chad, Pakistan, India, and South American nations. Guava trees can suffer from a variety of ailments, including Canker, Dot, Mummification, and Rust. A diagnosis based only on visual observation is unreliable and time-consuming. To help farmers identify plant diseases in their early stages, an automated diagnosis and prediction system is necessary. Therefore, we developed a deep learning method for classifying and forecasting guava leaf diseases. We investigated a dataset composed of 1834 leaf examples, separated into five categories. We trained the dataset using four different and generally preferred pre-trained CNN architectures. The EfficinetNet-B3 architecture outperformed the other three architectures, achieving 94.93% accuracy on the test data. The results ensure that deep learning methods are more successful and reliable than traditional methods.