基于监督局部线性嵌入算法的玉米田间杂草识别  

Recognition of weed in corn field based on supervised locally linear embedding algorithm

作　　者：阎庆[1,2] 梁栋[1] 张东彦[1,3] 

机构地区：[1]安徽大学计算智能与信号处理教育部重点实验室,合肥230601 [2]安徽大学电气工程与自动化学院,合肥230601 [3]国家农业信息化工程技术研究中心,北京100097

出　　处：《农业工程学报》2013年第14期171-177,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(61172127;51277002);高等学校博士学科点专项科研基金(20113401120006);安徽大学"211工程"青年科学研究基金(KJQN1114);安徽省高等学校省级自然科学研究项目(KJ2013A026);安徽省自然科学基金项目(1308085QC58)

摘　　要：杂草精准识别是实现农药定向定量喷洒的基础,是精准农业重要的研究课题之一,对环境保护和生产成本控制都有着重要的现实意义。该文以玉米田间常见杂草为研究对象,首先通过超绿特征去除田间复杂背景的影响,然后采用形态学方法自动分割图像中绿色植物区域作为待判别为杂草或作物的识别对象,之后采用基于Fisher投影的监督LLE(locally linear embedding)方法对样本的高维灰度特征进行降维,在低维空间结合支持向量机实现了杂草的快速识别。试验结果表明,该识别方法能更好地发现杂草与玉米的低维特征,对杂草和玉米植株的平均识别率分别达到97.2%和77.8%。该研究结果可为精准喷洒除草剂的自动化实现提供参考。Large-scale pesticide spraying will raise costs in agriculture and will cause environmental pollution. In order to realize quantitative and directional spraying, weed identification using image-processing technology is one of the focus problems in the precision-agriculture field. The foundation of automated identification is feature extraction. Because the dimensions of the feature are usually very high, before identification the dimensions must be reduced. The performance of any dimension-reduction method will directly affect the recognition results. The traditional dimension reduction method is a linear method, so it is very difficult to grasp the nonlinear nature of the original data. Locally linear embedding （LLE） is one kind of emerging manifold method. Compared with the traditional method, it is a nonlinear method, but it still has some limitations. The locally linear embedding is essentially an unsupervised method, and it cannot utilize the category information of the train samples. The traditional locally linear embedding method has defects in dealing with the classification problem, so the recognition accuracy is not satisfactory. In order to overcome this defects above, a supervised locally linear embedding method based on Fisher projection （FS-LLE） was chosen to reduce the feature dimension. The samples are projected by Fisher transformation first. Then, the projection coordinates are obtained, and projection distances are computed. The Euclidean distance is used to select the sample points＇ neighbors in the traditional locally linear embedding. Compared with the Euclidean distance, the Fisher projection distances can characterize the category attributes between different types of samples, so it is chosen to determine the neighborhood structure. In order to verify the effectiveness of this dimension reduction method, the experiment is designed as follows. The weed images are collected from the field, and the grayscale features are obtained. The influence of land background is excluded by supe

关 键 词：图像处理 识别 算法 监督局部线性嵌入 支持向量机 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]