Abstract: For the shortcomings of traditional disease recognition systems that require high altitude in a shooting environment and large numbers of samples, this research designed a set of f augmented reality (AR)-assisted recognition schemes based on histograms of oriented gradient (HOG)-support vector machine (SVM). Under the premise of a small amount of materials, this solution, which introduced AR technology in the diagnostic system for shooting assistance, outperforms 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 rice disease identification system was 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 were analyzed from three aspects: disease accuracy, diseased leaf detection rate, and disease spot location accuracy. Finally, AR technology and rapid identification scheme based on HOG-SVM were obtained. This combination can generate faster training results and recognition results under the premise of small training samples. The average accuracy of this system is also higher than that of deep models such as YOLO v3, SSD 512, and Fast R-CNN. The proposed method is more practicable for disease identification on the current mobile terminal. 

Key words: augmented reality, histograms of oriented gradient, support vector machine, rice, disease recognition