基于无人机图像处理的大豆叶片病害识别准确率研究

Research on Identification Accurate Rate of Soybean Leaf Diseases Based on UAV Image Processing

为了平衡无人机识别覆盖度与识别准确率之间的矛盾,实现大面积大豆病害的精确识别,利用简单线性迭代聚类(Simple linear literative clustering,SLIC)超像素方法处理无人机在高度为1、2、4、8、16 m获取的大豆叶片图像,提取叶片的颜色、梯度、纹理、形状特征后,基于这些属性的特征向量利用序列最小优化(Sequential minimal optimization,SMO)、J48决策树、最邻近(K-nearest neighbors,KNN)、随机森林算法对大豆叶片病害情况进行识别,得到不同算法对病害识别的准确度,并通过对比特征融合和单一特征时的病害识别准确度,分析输入参量对病害识别的影响。结果表明,无人机图像的拍摄高度为1 m和2 m时4种算法的准确率均在90%以上,SMO算法和随机森林算法的识别准确率较高。以融合特征向量为输入参量对大豆病害的识别准确率要高于单一特征向量,颜色是最能反映叶片病害实际情况的属性,无人机的最佳拍摄高度为1~2 m。

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.