关键词: 炭疽病, 深度学习, 目标检测, 分类识别, 病害诊断, SVM

Abstract:

Anthracnose is a pervasive and serious disease in citrus orchards. In order to improve the accuracy and efficiency of disease identification under orchard environmental conditions and ensure fruit yield and quality， this study recognized the ROI fusion features of diseases image in orchard. A dataset comprising of 9 types of citrus anthrax images depicting various disease sites and stages was collected for model training purposes. In the disease ROI feature extraction and detection module， image color， texture features， and their fused features were extracted to obtain more disease feature information， and form an SVM classifier. The trained SVM classifier was used to detect and identify the disease images to be tested. The trained SVM classifier successfully detected and recognized the target disease images by fusing spectral and texture features， the average accuracy rate of disease identification can reach 94%， with an average processing time for disease identification of 0.005 s. This method had high accuracy and strong robustness for the detection and recognition of citrus anthracnose in complex natural environments， and was of great significance for the prevention and control of citrus diseases.

Key words: anthracnose, deep learning, object detection, classification recognition, disease diagnosis, SVM