Convolutional Neural Network (CNN) Architecture for Pest and Disease Detection in Agricultural Crops

Abstract

The ravages of insect pests and plant diseases cause a profound loss in crops. Sometimes, pests and diseases are difficult to identify in the early stages through visual assessment and detection is not possible for larger areas. With the advancement, various technologies have been employed in the agricultural sector for successful crop production. Convolutional Neural Network (CNN) is the deep learning model used to classify the image data into an output variable. This advanced approach is much more practical than human supervision for the detection of insect pests and diseases in crops. It can able to identify pests and diseases with maximum accuracy. The CNN architectures viz., InceptionV3, DenseNet201, ResNet50V2, Visual Geometry Group (VGG19) and Regional Proposal Network (RPN) have been discussed here.