基于多光谱图像和数据挖掘的多特征杂草识别方法  

Identification method of multi-feature weed based on multi-spectral images and data mining

作　　者：赵川源[1] 何东健[1] 乔永亮[1] 

机构地区：[1]西北农林科技大学机电学院,杨凌712100

出　　处：《农业工程学报》2013年第2期192-198,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金资助项目(60975007;31000670)

摘　　要：为满足变量喷洒对杂草识别正确率的要求,提出一种基于多光谱图像和数据挖掘的杂草多特征识别方法。首先对多光谱成像仪获取的玉米与杂草图像从CIR转换到Lab颜色空间,用K-means聚类算法将图像分为土壤和绿色植物,随后用形态学处理提取出植物叶片图像,在此基础上提取叶片形状、纹理及分形维数3类特征,并基于C4.5算法对杂草分别进行单特征和多特征组合的分类识别。试验结果表明,多特征识别率比单特征识别率高,3类特征组合后的识别率最高达到96.3%。为验证该文提出方法的有效性,将C4.5算法与BP算法以及SVM算法进行比较,试验结果表明C4.5算法的平均识别率高于另2种算法,该文提出的田间杂草快速识别方法是有效可行的。该文为玉米苗期精确喷洒除草剂提供技术依据。Field weed detection is one of the key problems in realizing the variable precision applying pesticide to take place of the herbicide. Image-based weed classification and spectral information of plants are useful to detect weeds in real-time using multi-spectral features. Aimed to meet the identification accuracy requirements of variable spraying on weed, a new method using decision tree algorithm-C4.5 of data mining was developed to discriminate or classify crop and weeds by the multi-spectral images. The multi-spectral images of weeds and maize were captured by MS4100 Duncan Camera in the test field of Northwest Agriculture and Forestry University on May, 2012, and transformed from CIR color space to Lab systems, which can distinguish different quantized color and measure the Euclidean distance of different colors. Then vegetation was segmented from soil using K-means clustering algorithm. Mathematical morphology was used to fill small holes among the extracted vegetation leaves, and connect the uncompleted contour line of the discontinuous edges which may be caused by noise, occlusion and other factors. Contour tracing was used to get the contours of leaves. After these image processing, shape features, texture features and fractal dimensions of the vegetation were extracted. A random sample of 120 images from all 240 images were involved in this study as the training samples, 20 images from 40 images were used as the test samples. The results of statistic analysis showed that multi-feature combining with shape feature, texture feature and fractal dimension together achieved the highest recognition rate of 96.3%, compared to the single feature recognition rate of 75.0%. To validate the feasibility of this study, C4.5 algorithm was compared with BP （error back propagation） algorithm and SVM （support vector machine） algorithm in recognizing multi-feature. The experimental results showed the average recognition rates were 92.5% and 95.0%for BP and SVM algorithms, respectively. The results showed that the

关 键 词：数据挖掘 聚类算法 图像处理 杂草识别 多光谱图像 多特征识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] S126[自动化与计算机技术—计算机科学与技术]