• Sushmita S
• Sparsha Bindu B S
• Dr. S Srividhya

Farmers in India must be able to correctly categorize areca nuts as healthy or harmful because they are a significant agricultural crop. We suggest a method that automatically categorizes areca nuts based on unprocessed photos with husks using image processing techniques. To extract features from areca nut images, our system uses segmentation using the Otsu method and the GLCM texture feature extraction approach. Based on the recovered texture data, we classify the areca nuts using both Convolutional Neural Networks (CNNs) and decision trees. Although the decision tree model classifies the areca nuts using a tree-like structure and decision rules, the CNN model is trained using a huge dataset of areca nut pictures. Our system has a high level of accuracy.

Image Processing; Median filter; Otsu segmentation; GLCM; CNN; Decision Tree; Classification;

"Arecanut Disease Detection Using Image Processing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.c387-c392, May-2023, Available :http://www.jetir.org/papers/JETIR2305257.pdf

"Arecanut Disease Detection Using Image Processing", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppc387-c392, May-2023, Available at : http://www.jetir.org/papers/JETIR2305257.pdf