Akademik Veri Yönetim Sistemi
World Scientific and Engineering Academy and Society (WSEAS), cilt.20, sa.38, ss.356-363, 2023 (Hakemli Dergi)
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.