Authors : Cithi Farhana S; Jancy I; Athilakshmi M

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/4dztbm9c

Scribd : https://tinyurl.com/2tas6tfu

DOI : https://doi.org/10.5281/zenodo.14869976

Abstract : In order to process and segment leaf photos for the purpose of forecasting and classifying illnesses, deep learning classification algorithms have been the subject of numerous studies. The correlations between banana yield and suggested disease prediction indicators are presented in this article for the primary banana-exporting type in India.During the examination phase, only pixels that had a 100% banana plant were deemed diseased. The prediction and identification stages make use of image processing, image segmentation, and picture capturing.Here To determine the pathogen affecting banana plants, an image processing technique was used. K-means clustering is used to extract one of the clusters comprising the diseased areas after segmentation. For the goal of classifying bananas for disease, the Stacked RNN is employed. This prediction and classification of leaf diseases produces the greatest results in terms of accuracy and computing efficiency when compared to other models that are currently in use. This disease prediction and categorization is implemented using the Tensor Flow and Keras libraries. The performance is estimated using the f-measure, exactness, and review metrics. The activations used in the sickness classification procedure are ReLu and SoftMax, while the optimization technique is Adam.

Keywords : Image Processing; Segmentation; K-Means Clustering; Stacked RNN.

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