Carrot Cure: A CNN based Application to Detect Carrot Disease

Abstract

Carrot is a famous nutritional vegetable and developed all over the world. Different diseases of Carrot has become a massive issue in the carrot production circle which leads to a tremendous effect on the economic growth in the agricultural sector. An automatic carrot disease detection system can help to identify malicious carrots and can provide a guide to cure carrot disease in an earlier stage, resulting in a less economical loss in the carrot production system. In this paper, we have developed a web application “Carrot Cure” based on Convolutional Neural Network (CNN) which can identify a defective carrot and provide a proper curative solution. Images of carrots affected by cavity spot and leaf bright as well as healthy images were collected. In this research, we’ve employed Convolutional Neural Network to include birth neural purposes and a Fully Convolutional Neural Network model (FCNN) for infection order. We’ve explored different avenues regarding different convolutional models with colorful layers and the proposed Convolutional model achieved the perfection of virtually 99.8%, which is surely useful for the drovers to distinguish carrot illness and boost their advantage. Index Terms—Carrot Disease Detection, Image Processing, Web Application, Convolutional Neural Network, CNN Model, Deep Learning Approach