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.
翻译:胡萝卜是一种世界各地都种植的著名营养蔬菜。不同的胡萝卜疾病已经成为胡萝卜生产中的一个巨大问题,这导致农业部门的经济增长受到极大影响。自动胡萝卜疾病检测系统可以帮助检测有恶意的胡萝卜,提供早期治疗指南,从而减少胡萝卜生产系统中的经济损失。所提出的研究在卷积神经网络(CNN)的基础上开发了一个名为胡萝卜治疗的Web应用程序,可以识别有缺陷的胡萝卜并提供适当的治疗方案。收集了空腔病斑和叶亮病受影响的胡萝卜图像以及健康图像。此外,本研究采用了卷积神经网络来包括出生神经目的和全卷积神经网络模型(FCNN)进行感染排序。尝试使用不同的彩色图层卷积模型,所提出的卷积模型达到了99.8%的完美度,这将有助于牧民识别胡萝卜疾病并提高他们的利润。