AR-assisted intelligent analysis and identification system for mobile vegetables diseases based on HOG-SVM

Aiming at the shortcomings of traditional disease recognition systems that require high shooting environment and large number of samples, this research designs a set of AR-assisted recognition schemes based on HOG-SVM. Under the premise of a small amount of material, due to the introduction of AR technology in the diagnostic system to assist shooting, this solution is better than other methods in terms of training time, recognition speed and average accuracy. Taking the Android terminal as an example, an AR-assisted HOG-SVM-based mobile vegetables disease identification system is implemented, which can quickly identify diseases and guide users to improve the quality of photographed pictures. Through the identification of disease spots in batches of images, the results of disease spot recognition are analyzed from the three aspects of disease accuracy, diseased leaf detection rate and disease spot location accuracy. Finally, AR technology and rapid identification scheme based on HOG-SVM are obtained. The combination can give faster training results and recognition results under the premise of small training samples. Its average accuracy is also higher than deep models such as YOLO v3, SSD 512, and Fast R-CNN. It is a more suitable method for disease identification on the current mobile terminal.