A Comprehensive Review on Banana Leaf Disease Detection Using Modern Learning Methodologies

Bananas serve as a crucial worldwide agricultural product, yet leaf diseases considerably hinder plant development and production. Timely and precise identification of diseases is crucial to halt their spread and promote sustainable faring practices. This review article discusses existing research on the identification of banana leaf diseases using image processing, machine learning, and deep learning techniques, using publicly available datasets and real-world images. Existing research suggests that deep learning-based methods generally outperform traditional methods. However, accuracy and generalization are highly impacted by class imbalance, preprocessing methods, and limitations of datasets. Automated disease identification systems provide effective solutions for identifying diseases in banana leaves. However, challenges such as limited datasets and real-world differences continue to exist, emphasizing the need for further research to ensure reliable agricultural applications.