A comprehensive deep neural network approach for multi- class classification of Bangladeshi local bananas with ripeness assessment

Abstract

This research employs the NasNetMobile model to address the critical task of banana disease classification, achieving an impressive accuracy rate of 97.12%. Leveraging deep learning techniques, the study focuses on enhancing precision agriculture by accurately identifying and classifying various diseases affecting banana plants. The robust performance of the NasNetMobile model underscores its potential for revolutionizing disease identification methodologies in the agricultural sector. Attaining high accuracy is a promising step towards improving crop management practices and optimizing resource allocation. This research contributes valuable insights into the intersection of deep learning and precision agriculture, paving the way for more effective strategies in banana disease monitoring and mitigation.