Banana Leaf Disease Detection Using Deep Learning
Keywords:
Banana leaf disease, Deep learning, CNN, Precision agriculture, Plant disease detection, Image classificationAbstract
Banana is one of the most important fruit crops cultivated in tropical and subtropical regions. Banana production is significantly affected by leaf diseases such as Black Sigatoka, Yellow Sigatoka, Panama Wilt, and Banana Bunchy Top Virus. Early and accurate identification of these diseases is essential to reduce crop loss and improve productivity. Traditional disease diagnosis depends on visual inspection by agricultural experts, which is time-consuming, subjective, and often unavailable in rural regions. This paper proposes a deep learning-based banana leaf disease detection system using Convolutional Neural Networks (CNNs). The proposed framework performs image preprocessing, leaf segmentation, feature extraction, and disease classification. The model is evaluated on publicly available banana leaf disease datasets and achieves high classification accuracy. Experimental results demonstrate that the proposed approach can accurately distinguish healthy and diseased banana leaves and therefore can support precision agriculture applications.
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