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
