Carrot Disease Recognition using Deep Learning Approach for Sustainable Agriculture

Abstract

Carrot is a fast-growing and nutritious vegetable cultivated throughout the world for its edible roots. The farmers are still learning the scientific methods of carrot production worldwide. For the production of good quality carrots, modern technology is not being used to its fullest to detect carrot vegetable diseases in the farms. As a result, the farmers face difficulties now and then in continuous monitoring and detecting defects in carrot crops. Hence, this paper proposes an efficient carrot disease identification and classification method using a deep learning approach, especially Convolutional Neural Network (CNN). In this research, five different carrot diseases including healthy carrots have been examined and experimented with four different pretrained models of CNN architecture, i.e., VGG16, VGG19, MobileNet, and Inception v3. Among the four models, the Inception v3 model is selected as an efficient pretrained CNN architecture to build an effective and robust system. The Inception v3 based system proposed here takes carrot images as input and examines whether they are healthy or infected, and provides output accordingly. To train and evaluate the system, a robust dataset is used, which consists of original and synthetic data. In the Fully Connected Neural Network (FCNN), dropout is used to solve the problem of overfitting as well as to improve the accuracy of the system. The accuracy achieved from the method which uses Inception v3 is 97.4%, which is undoubtedly helpful for the farmers to identify carrot disease and maximize their benefits to establish sustainable agriculture.