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.
