Institute of Physical Biology, University of South Bohemia, Zámek 136, 37333 Nové Hrady, Czech Republic.
J Fluoresc. 2009 Sep;19(5):905-13. doi: 10.1007/s10895-009-0491-x. Epub 2009 May 16.
Automatic discrimination of plant species is required for precision farming and for advanced environmental protection. For this task, reflected sunlight has already been tested whereas fluorescence emission has been only scarcely considered. Here, we investigated the discriminative potential of chlorophyll fluorescence imaging in a case study using three closely related plant species of the family Lamiaceae. We compared discriminative potential of eight classifiers and four feature selection methods to identify the fluorescence parameters that can yield the highest contrast between the species. Three plant species: Ocimum basilicum, Origanum majorana and Origanum vulgare were grown separately as well as in pots where all three species were mixed. First, eight statistical classifiers were applied and tested in simulated species discrimination. The performance of the Quadratic Discriminant Classifier was found to be the most efficient. This classifier was further applied in combination with four different methods of feature selection. The Sequential Forward Floating Selection was found as the most efficient method for selecting the best performing subset of fluorescence images. The ability of the combinatorial statistical techniques for discriminating the species was also compared to the resolving power of conventional fluorescence parameters and found to be more efficient.
精确农业和先进的环境保护都需要对植物物种进行自动识别。为此，人们已经测试了反射光，而荧光发射则很少被考虑。在这里，我们使用唇形科的三个密切相关的植物物种进行了案例研究，研究了叶绿素荧光成像的判别潜力。我们比较了八种分类器和四种特征选择方法的判别潜力，以确定可以在物种之间产生最大对比度的荧光参数。我们分别种植了三种植物：罗勒、牛至和普通牛至，以及三种植物混合种植在花盆中。首先，我们应用并测试了八种统计分类器来模拟物种识别。结果发现二次判别分类器的性能最为有效。然后，我们将这个分类器与四种不同的特征选择方法相结合。顺序前向浮动选择被发现是选择最佳荧光图像子集的最有效方法。组合统计技术识别物种的能力也与传统荧光参数的分辨率进行了比较，发现其效率更高。
