Deep Learning Based Detection of Corn Stalk Diseases with XAI

Ravishka Ranasinghe; Guhanathan Poravi1

1

Publication Date: 2024/12/04

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.

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

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24NOV1036.pdf

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