IDENTIFICATION AND DETECTION OF PESTS IN PLANTS USING DARKNET-53 AND YOLO ALGORITHM

Agriculture is India’s primary source of income. However, pests have harmed the primary growth of crops like tomato, rice, maize, and wheat. Crop yield has reduced due to a large number of insects. Food supplies become insufficient. Farmers have difficulty classifying and identifying the insects that devastate growth in the early phases. A computer vision-based approach is utilized in this study to recognize and classify all of the many types of infecting insects that affect plant growth. A convolution neural network was built using the Darknet and Yolo architectures to classify insects in the agriculture sector. The proposed network has a 96 percent accuracy in detecting dangerous bugs.