利用计算机视觉和光谱分割技术进行水稻叶片钾胁迫特征提取与诊断研究  

Leaf Characteristics Extraction of Rice under Potassium Stress Based on Static Scan and Spectral Segmentation Technique

作　　者：石媛媛[1,2] 邓劲松[1,2] 陈利苏[1,2] 张东彦[1,2] 丁晓东[1,2] 王珂[1,2] 

机构地区：[1]浙江大学农业遥感与信息技术应用研究所/浙江省农业遥感与信息技术应用重点实验室,浙江杭州310029 [2]浙江大学环境修复与生态健康教育部重点实验室,浙江杭州310029

出　　处：《光谱学与光谱分析》2010年第1期214-219,共6页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(30571112;30800703);国家"863"项目(2006AA10Z204);国家博士后基金项目(20070421194);浙江科技项目(2007C23089;2008C33008)资助

摘　　要：实时、便捷、可靠的作物营养诊断和监测方法是科学施肥的基础。传统手段在取样、测定、数据分析方面需耗费大量的人力、物力,且时效性差。通过静态扫描技术采集不同钾营养水平的水稻叶片图像,利用面向对象的光谱分割技术和最近邻分类器,根据扫描图像中目标对象的光谱、空间、形状等特征对钾胁迫叶片特征进行了准确的提取和识别,并从分类结果里初步判断出斑点区域面积比例随钾浓度的增大而减小,用叶片图像进行缺钾叶片量化诊断时,第三完全展开叶优于第一完全展开叶。随机选取250个点利用误差分析矩阵方法进行精度评价,总体识别精度为96.00%,KAPPA系数为0.945 3。这一叶片特征提取方法为水稻钾胁迫量化诊断提供了新的方法。The timing, convenient and reliable method of diagnosing and monitoring crop nutrition is the foundation of scientific fertilization management. However, this expectation cannot be fulfilled by traditional methods, which always need excessively work on sampling, detection and analysis and even exhibit lagging timing. In the present study, stable images for potassiumstressed leaf were acquired using stationary scanning, and object oriented segmentation technique was adopted to produce image objects. Afterwards, nearest neighbor classifier integrated the spectral, shape and topologic information of image objects to precisely identify characteristics of potassium-stressed features. Diagnosing with image, the 3rd expanded leaves are superior to the 1st expanded leaves. In order to assess the result, 250 random samples and an error matrix were applied to undertake the accuracy assessment of identification. The results showed that the overall accuracy and kappa coefficient was 96.00% and 0. 945 3 respectively. The study offered an information extraction method for quantitative diagnosis of rice under potassium stress.

关 键 词：扫描 光谱分割 钾胁迫 信息提取 

分 类 号：TP391[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]