Leveraging Deep Learning for Early and Accurate Pre-diction of Banana Crop Diseases: A Classification and Risk Assessment Framework

Main Article Content

Walter Ocimati
Sivalingam Elayabalan
Nancy Safari


Banana crop diseases significantly threaten global food security and farmer livelihoods. Traditional visual inspection methods are subjective, time-consuming, and ineffective for early disease detection. This study addresses these challenges by proposing a novel framework utilizing deep learning for early and accurate prediction of banana crop diseases. Our objective is to develop a two-pronged approach: 1) disease classification using deep learning to analyze images and identify healthy or diseased plants, and 2) risk assessment incorporating weather patterns and historical disease data to predict areas susceptible to outbreaks. This combined approach offers a more comprehensive disease management strategy compared to traditional methods. We plan to utilize a dataset of banana leaf images labeled with disease status and relevant environmental data. The performance of the deep learning model will be evaluated using metrics like accuracy, precision, and recall for disease classification. This data-driven framework has the potential to revolutionize banana crop disease management by enabling early detection, targeted prevention strategies, and ultimately, increased yield, reduced economic losses for farmers, and enhanced food security.

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How to Cite
Walter Ocimati, Sivalingam Elayabalan, and Nancy Safari, “Leveraging Deep Learning for Early and Accurate Pre-diction of Banana Crop Diseases: A Classification and Risk Assessment Framework”, Int. J. Comput. Eng. Res. Trends, vol. 11, no. 4, pp. 46–57, Apr. 2024.
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