State of the Art Deep Learning Implementation for Multiclass Classification of Black Pepper Leaf Diseases

Abstract

Abstract Black pepper is a medicinal plant that is extensively used in Ayurvedic medicine because of its therapeutic properties. Leaf diseases can be diagnosed at an early stage with the aid of a smart computer vision system and timely disease prevention can be targeted. The proposed work represents an intelligent transfer learning technique through state-of-the-art deep learning application to predict the presence of prominent diseases in black pepper leaves. The ImageNet dataset available online is used for training deep neural network, initially. Later, this trained network is utilized for the prediction of the developed black pepper leaf image dataset. The developed data set consist of real time leaf images, which are candidly taken from the fields and annotated under supervision of an expert. The leaf diseases considered including healthy leaves are anthracnose, slow wilt, early stage phytophthora, phytophthora and yellowing. The accuracy obtained with 0.001 learning rate ranges from 99.1–99.5% for the Inception V3, GoogleNet, SqueezeNet and Resnet18 models. This work represents improvement in agriculture and a cutting edge deep neural network method for early stage leaf disease identification and prediction. This is an approach using a deep learning to predict black pepper leaf disease.