• Prajwal Annasab Chougale
• Avinash K
• Ashok Ammanagi
• Mohammed Tayyab Hussain
• Byregowda B K

Arecanut, sometimes referred to as betel nut, is a tropical crop. India is the world's second-largest producer and consumer of arecanuts. It is afflicted by a number of illnesses from root to fruit throughout its life cycle. The only method used to identify illnesses at the moment is visual inspection, and farmers must periodically examine each crop carefully to look for infections. In this study, we suggested a system that, with the use of convolutional neural networks, aids in identifying arecanut disorders and offers possible treatments. An image is used as the input for a convolutional neural network (CNN), which then gives different objects in the image learnable weights and biases. 888 photos of healthy and ill arecanuts were used to generate our own dataset. The ratio between the train and test data is 80:20. Categorical cross-entropy is employed as the loss function for model construction, with Adam serving as the optimizer function and accuracy serving as the metrics. The model is trained over the course of 50 epochs in order to maximize validation and test accuracy while minimizing loss. The proposed method identified the arecanut illness with an accuracy rate of 88.46% and was shown to be effective.

Arecanut, artificial intelligence, and convolutional neural networks.

"CLASSIFICATION AND DECTECTION OF DISEASES OF ARECA NUT AND LEAF USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.d564-d571, May-2023, Available :http://www.jetir.org/papers/JETIR2305376.pdf

"CLASSIFICATION AND DECTECTION OF DISEASES OF ARECA NUT AND LEAF USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppd564-d571, May-2023, Available at : http://www.jetir.org/papers/JETIR2305376.pdf