面向植物生长智能监控的叶颜色特征提取

根据植物生长智能监控的需求，设计了叶颜色特征提取方法：首先提取叶整体颜色特征，根据特征数据能够判断植物叶是健康、存在整体颜色变异还是存在局部颜色变异；如果存在局部颜色变异，进行局部颜色变异部分分割、颜色特征提取和空间分布特征提取。将一组具有不同程度颜色变异的植物叶的阳面和阴面图像作为两个系列进行了对比测试。测试数据表明：特征参数能够区分健康的叶和存在变异的叶，能够区分不同类型和不同程度的颜色变异，能够为后续决策处理提供数据支持，特征提取方法适合植物生长智能监控。

Leaf color feature extraction designed for intelligent monitoring of plant growth

Color feature extraction of plant leaf is a key step in intelligent monitoring of plant growth. A color feature extraction method of plant leaf has been designed according to the requirements of intelligent monitoring of plant growth. The overall color feature of plant leaf is extracted at first. The feature data can be used to identify that a leaf is healthy, has overall color variation or has local color variation. If local color variation has been detected, it will be segmented from the leaf and then its color and spatial distribution features will be computed. Tests have been conducted using leafs with different variations, in which the sunny side and the shade side of each leaf are set as two different series for comparison. The data showed the feature parameters designed could provide data to judge a leaf healthy or not, including the type and extent of color variation. The method is applicable to intelligent monitoring of plant growth and it can provide data for the following decision-making process.