Tomato Crop Disease Classification using Convolution Neural Network and Transfer Learning

Abstract

Agriculture struggles to cater to the rapidly increasing global population, one major cause for this are the plant diseases and pests which negatively hinder the production quantity and quality of food, fibre and biofuel crops. In some parts of the world, losses in tomato production due to pests continue to exceed a staggering 50% of attainable production. This paper aims to utilize DL algorithms such as CNN (Convolution Neural Network) to detect multiple diseases in tomato plant. One limitation of the current CNN models is that it does not perform well with small datasets and fails in cases of specimen having symptoms of multiple diseases or viruses in the same image of the dataset. This paper aims to fix that