摘要
以越冬期冬小麦冠层可见光图像为对象,研究基于图像特征的含水率检测方法。采用同态滤波与多尺度Retinex相结合的光照增强算法,消除自然条件下光照不均匀和颜色失真的影响,提取颜色、纹理和形态等39个初始图像特征,采用相关分析和假设检验进行显著特征筛选,并运用偏最小二乘回归建立冠层含水率检测模型。对淮麦30和烟农19 2个冬小麦品种的测试结果显示,检测相对误差均值为1.290%,方差为1.053,2个品种之间没有明显差异,而晴天、中午的检测误差稍大,表明研究的方法具有较高的检测精度和良好的适应性。
In order to accurately and easily determine the canopy moisture content of winter wheat during wintering period,methods of image processing and feature application based on visible light image were researched. According to the illumination invariance and color constancy principle,the combinational algorithm of homomorphic filtering and multi-scale Retinex was proposed for illumination enhancement processing to eliminate the adverse effects of natural light condition. Totally 39 initial image features which belonged to color,texture and morphology were extracted and investigated,remarkable features selection was conducted by correlation analysis and hypothesis testing. Partial least squares regression was then adopted to establish the water content detection model of canopy. Test results for two winter wheat varieties of ＂Huai-mai 30＂and ＂Yan-nong 19＂showed that the mean relative error and variance of the proposed method were 1. 290% and 1. 053,respectively,which had no obvious differences between the two varieties,and the detection errors were slightly large in sunny days and noon. The results indicated that the proposed method had high detection accuracy and good adaptability. The key issues of the field image enhancement and image feature selection were studied,and the results are helpful to improve the practicability of crop moisture detection based on the computer vision technology under the background of agricultural internet of things. Meanwhile,the canopy moisture content detection model of winter wheat during wintering period which was established based on this method has good performance,and it can provide effective technical support for winter wheat freeze-proofing and drought resistant decision.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2015年第12期260-267,共8页
Transactions of the Chinese Society for Agricultural Machinery
基金
安徽省自然科学基金资助项目(1508085MF110)
安徽省科技攻关资助项目(1501031102)
引进国际先进农业科学技术计划(948计划)资助项目(2015-Z44)
关键词
冬小麦
冠层含水率
检测模型
图像处理
特征筛选
偏最小二乘回归
Winter wheat
Canopy moisture content
Detection model Image processing
Feature selection
Partial least squares regression
作者简介
作者简介：江朝晖，副教授，博士，主要从事农业信息检测与处理研究，E-mail：jiangzh@ahau．edu．cn
