Carrot Disease Recognition using Deep Learning Approach for Sustainable Agriculture

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