BANANA LEAF DISEASE PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS

Authors

  • R. Jeyachandra , D. Vasumathi

Keywords:

Banana Leaf Disease; Machine Learning; Deep Learning; Classification; Convolutional Neural Network.

Abstract

Banana leaf diseases such as Black Sigatoka, Fusarium Wilt, and Bacterial Wilt pose a significant threat to global banana production, affecting both yield and quality. Early detection and classification of these diseases are critical for mitigating losses and improving crop management. This study proposes a deep learning-based solution using Convolutional Neural Networks (CNNs) to predict and classify banana leaf diseases from image data. The dataset, comprising images of healthy and diseased banana leaves, was pre-processed and augmented to enhance model performance.

References

Raj, E. Fantin Irudaya, M. Appadurai, and K. Athiappan. "Precision farming in modern agriculture." Smart agriculture automation using advanced technologies: Data analytics and machine learning, cloud architecture, automation and IoT. Singapore: Springer Singapore, 2022. 61-87. 2. Raj, E. F. I., Appadurai, M., Thiyaharajan, D., & Pushparaj, T. L. (2024). State-Of-TheArt Technologies for Crop Health Monitoring in the Modern Precision Agriculture. In

Precision Agriculture for Sustainability (pp. 21-39). Apple Academic Press.

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Published

2024-10-12

How to Cite

R. Jeyachandra , D. Vasumathi. (2024). BANANA LEAF DISEASE PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 1881–1894. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1896

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