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. The proposed research study has 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. Further,
this research work has employed Convolutional Neural Network to include birth
neural purposes and a Fully Convolutional Neural Network model (FCNN) for
infection order. Different avenues regarding different convolutional models
with colorful layers are explored and the proposed Convolutional model has
achieved the perfection of 99.8%, which will be useful for the drovers to
distinguish carrot illness and boost their advantage.

研究提出了一个基于卷积神经网络 (CNN) 的网络应用程序 Carrot Cure，可以识别有缺陷的胡萝卜，并提供适当的治疗方案，该卷积模型已经达到了 99.8% 的完美度，将有助于驾驶员区分胡萝卜疾病并提高他们的优势。