Banana Leaf Disease Classification Using CNN Feature Extraction and Naive Bayes Algorithm

Abstract

Banana leaf diseases such as Black Sigatoka, Cordana, and Pestalotiopsis significantly reduce productivity and require early, accurate detection to prevent severe yield losses. While Convolutional Neural Networks (CNN) have demonstrated high performance in plant disease classification, most existing approaches rely on computationally intensive end-to-end deep learning models, limiting their deployment on resource-constrained devices. This study proposes a lightweight hybrid classification framework that integrates MobileNetV2-based CNN feature extraction with a Gaussian Naive Bayes classifier. The novelty of this research lies in the systematic transformation of deep 1,280-dimensional feature representations into a probabilistic classification space, enabling competitive accuracy with substantially lower computational complexity. A balanced dataset consisting of 3,200 training images and 1,311 testing images collected from Pamekasan Regency was preprocessed through resizing, normalization, and augmentation. Experimental results show that the end-to-end CNN achieved 98.70% accuracy, while the proposed hybrid CNN–Naive Bayes model attained 95.73% accuracy with F1-scores above 0.90 across all classes. Despite not relying on backpropagation during classification, the hybrid approach maintains strong predictive performance while reducing training time and memory requirements. These findings demonstrate that integrating deep feature extraction with probabilistic learning provides an efficient and deployable solution for edge-based precision agriculture systems.