基于图像处理技术的小麦叶面积指数的提取

为了较好地模拟叶面积指数的变化动态，在大田条件下进行试验，获取5个品种5个密度下不同发育期的小麦群体冠层数字图像，并手工测得实际叶面积。通过研究设计了复杂背景下小麦冠层图像叶面指数的有效提取方法，将图像处理得到的叶面积指数数据与实际测得的数据进行拟合建立模型。结果表明：品种、密度和发育期的差异对拟合模型参数影响显著，对模型经过随机抽取样本图像进行假设检验，均能够通过检验。模型的相关系数平方均达到0.86以上，能够实现高精度的小麦冠层叶面积指数的估测。

Extraction of leaf area index of wheat based on image processing technique

For better simulating the dynamics of leaf area index (LAI), an experiment under field conditions, with five varieties and five densities at different stages, was carried out. The digital images of wheat groups canopy were obtained, , and the actual leaf area was manually measured. An effective extraction method of wheat LAI under complex background was designed. Simulation models between data of image processing and actual leaf area were established. Results showed that the model parameters were significantly affected by the differences of variety, density and growing stages. Hypothetical tests of the models were carried out by random extracting sample images, and all of them were able to meet the requirements of test. Square of the correlation coefficient of all models is higher than 0.86. This can achieve the higher precision estimation of wheat canopy LAI.