Authors : Ravishka Ranasinghe; Guhanathan Poravi

Volume/Issue : Volume 9 - 2024, Issue 11 - November

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Google Scholar : https://tinyurl.com/2nc7d463

Scribd : https://tinyurl.com/5h9a8ytn

DOI : https://doi.org/10.38124/ijisrt/IJISRT24NOV1036

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : This research aims at developing a deep learning model for corn stalk disease detection (for anthracnose disease) using explainable AI approaches, Grad-CAM. Based on the CNN deep learning models, the proposed system is developed. For inputs including images of corn stalks, the system prepares them, generates visual descriptions of the layer, and correctly categorizes the image as being related to a healthy corn plant or a diseased one. Overall, it plays a part in enhancing explainability of model predictions to the end user, especially the uninitiated in the aspect of some level of understanding. However, the system significantly saves training time and computational expense by using transfer learning without a decline in accuracy.

Keywords : Deep Learning, Grad-CAM, Convolutional Neural Networks, Image Classification, Explainable AI.

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