Hyperspectral selection based algorithm for plant classification

The popularity of using hyperspectral imaging systems in studying and monitoring plant properties and conditions has increased lately. This increase has been driven by both financial and environmental advantages of such systems. Using a nondestructive hyperspectral imaging system improves the breeding process, increases profit, and reduces the usage of herbicide, thus reducing side effects to plants and environment. This paper is concerned with the use of hyperspectral image analysis for differentiating different plant species as well as their conditions. The main contribution of the work lies in the use of feature selection for choosing relevant, discriminant spectral information as the input to the classifier (e.g. SVM), as compared to the use of empirical spectral indices. Two independent hyperspectral datasets, captured by different instrumentations, were used in evaluation. Experimental results show significant improvements in classification accuracy with several feature selection algorithms compared to with the spectral vegetation and disease indices. The study shows that systematically selection of wavelength features can shed light on attributes that differentiate plants and their conditions.