A Grapevine Virus Disease Detection Method Based on Convolution Neural Network

Black rot, black measles and isariopsis leaf spot are three kinds of very fatal grapevine virus disease. In the cultivation of grape, these diseases will harm the growth of grapes and have a great impact on the yield. Thus, timely diagnosis and treatment measures in the early stage of disease will greatly reduce the mortality of grape, which is particularly important in the cultivation of grape. The traditional method of manual screening requires staff with professional knowledge of diseases and detection experience, which requires high labor cost and a lot of time in large-scale detection. We consider adding a convolution neural network based deep learning detection method in large-scale screening to quickly detect easily diagnosed cases so as to focus on the hard-to-discern cases and reduce work pressure. In this paper, we propose a detection scheme using advanced deep learning framework to identify these three diseases with similar symptoms, locate their positions in image visualization and outline them accurately. Numerical results reveal that the detection scheme has great performance, and the high-performance configuration is obtained through several experiments.