Deep Learning-Based Early Detection of Banana Leaf Diseases Using Binary Classification

Registration ID: IJNRD_328469

Published ID: IJNRD2609148

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Ms. Sreelekshmi Sreekumaran, Dr. Prasadu Peddi

Agriculture, banana leaf, classification, disease detection, deep learning, MobileNetV2

Agricultural production depends heavily on the timely identification and management of crop diseases. Banana cultiva-tion is particularly affected when leaf infections are noticed only after symptoms have spread. In conventional farming, checking plants one by one requires considerable time and depends on the experience of the person carrying out the in-spection. This makes early and regular monitoring difficult, especially when a large number of plants are involved. This work presents an image-based deep learning method for identifying whether a banana leaf is healthy or infected. Rather than separating every disease into a different class, the study treats the problem as a binary classification task. This makes the model simpler and reduces the number of decisions required during prediction. Banana leaf images are supplied to the trained network, which learns visual characteristics associated with healthy and diseased leaves. MobileNetV2 is used in the study through transfer learning. The pretrained network is used to learn useful image fea-tures, while the final classification layers are adapted to the two classes considered in this work. The study also uses Grad-CAM to examine the areas that contribute to a prediction. This visualization is useful for checking whether the network is responding to visible parts of the leaf rather than relying mainly on the background. The resulting approach is intended as a practical aid for early screening of banana leaf disease. A transfer learning model based on MobileNetV2 is employed for feature extraction and classification. The model learns discriminative visual patterns associated with disease symptoms, such as discoloration and lesions. In addition, visualization techniques such as Grad-CAM are used to interpret the model’s predictions by highlighting important re-gions in the input image. The proposed method demonstrates high accuracy and reliability, making it suitable for real-time agricultural applications.

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Ms. Sreelekshmi Sreekumaran & Dr. Prasadu Peddi (September-2026). Deep Learning-Based Early Detection of Banana Leaf Diseases Using Binary Classification. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(9), b467-b474. https://ijnrd.org/papers/IJNRD2609148.pdf

Volume 11 Issue 9, September-2026

Pages : b467-b474

Paper Reg. ID: IJNRD_328469

Published Paper Id: IJNRD2609148

Research Area: Other area not in list

Author Type: Indian Author

Country: TRIVANDRUM, KERALA, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2609148.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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