Incorporating Deep Learning into the Diagnosis of Banana Leaf Spot Diseases for the Protection of Banana Crops

Year 2025, Volume: 31 Issue: 3, 780 - 794, 29.07.2025

Cihan Ünal

https://doi.org/10.15832/ankutbd.1579442

Abstract

Banana crops play a pivotal role in securing global food supplies and supporting economic stability. However, they are confronted with significant challenges stemming from a variety of diseases that not only diminish yields but also compromise the quality of the fruit. Artificial intelligence, especially deep learning, assumes a pivotal role in tackling this challenge by leveraging advanced algorithms and data analysis techniques to enhance disease detection and diagnosis in banana crops, thus contributing significantly to their protection and preservation. To address this challenge, we present the "Banana Leaf Spot Diseases (BananaLSD) Dataset" comprising images of major banana leaf spot diseases and healthy leaves, meticulously labelled by plant pathologists. Using deep learning models, including DenseNet-201, EfficientNet-b0, and VGG16, we achieved remarkable disease classification accuracy rates. DenseNet-201 achieved an impressive 98.12% accuracy. The study analyses performance metrics and visualization by grad-cam technique. These results underscore the potential of deep learning for precise banana leaf disease diagnosis, offering significant implications for crop preservation, economic stability, and global food security.

Keywords

Banana leaves , Classification , Deep learning , DenseNet-201 , EfficientNet-b0 , VGG16

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