Cassava Disease Detection Method Based on EfficientNet

Abstract

The monitoring method of cassava disease in this article is based on HSV color space and EfficientNet, which can improve the monitoring and early warning of cassava virus disease, avoid the transportation and selection of diseased stems, and provide the necessary detection technology to ensure the healthy production of cassava. The HSV color space is used for image preprocessing, which improves the detection accuracy of the target area and reduces the loss of information in the image preprocessing stage. The EfficientNet model trains the preprocessed leaf image and extracts multi-dimensional depth, width, and resolution features.