Detection Of Diseases In Arecanut Using Convolutional Neural Network

Arecanut is one of the major crops in India. Because of its higher market value, it is grown more in India and other subtropical regions. This plant is susceptible to a number of diseases that cause great losses to farmers. These diseases occur due to sudden changes in temperature and other climatic conditions; Early detection of the disease is very important. This work involves a focus on early and accurate disease detection to reduce losses for farmers. The proposed work uses RGB images as input and a Res-Net model. It then assigns learnable weights and biases to different objects in the image, then based on the results, it learns to distinguish between them. Global average pooling is used for image detection.