关键词: 大豆叶片, 无人机, 病害识别, 特征提取

Abstract: In order to balance the contradiction between unmanned aerial vehicle(UAV) recognition coverage and recognition accuracy, and realize the identification accurate rate of large-area soybean diseases,SLIC super-pixel method was used to process soybean leaf images acquired by UAV at 1 m、2 m、4 m、8 m、16 m.After extracting the color,gradient,texture and shape characteristics of leaves,sequential minimal optimization,J48 decision tree,k-nearest neighbors and random forest algorithm were used based on the feature vectors of these attributes to identify soybean leaf diseases,and the accuracy of different algorithms for disease identification was obtained.The influence of input parameters on disease identification was analyzed by comparing the accuracy of disease recognition with feature fusion and single feature.The results showed that the accurate rates of the four algorithms were more than 90% when the UAV image shooting height was 1 m and 2 m,and the SMO algorithm and random forest algorithm had higher identification accurate rate.The identification accurate rate of soybean disease with fusion feature vector as input parameter was higher than that of single feature vector. Color was the attribute that could best reflect the actual situation of leaf disease.The optimal shooting height of UAV was 1—2 m.

Key words: Soybean leaves, Unmanned aerial vehicle(UAV) , Disease identification, Feature extraction