Clustering of crop phenotypes by means of hyperspectral signatures using artificial neural networks

Hyperspectral imaging linked to subsequent neural networks based analysis has proven its suitability to unravel complex information in a number of different application areas, such as geology, defence, etc. The extension of this approach to crop plant research, plant breeding, and agriculture has started quite recently. Here, the image acquisition ranges from airborne sensing mainly for agricultural applications down to single leaf analysis in the context of precision and high-throughput plant phenotyping. All these applications have in common, that particular relevant compounds of the plant need to be determined by means of hyperspectral signatures as substitute to extensive biochemical analysis. This paper describes the quantitative assessment of a number of genetically different tobacco varieties (Nicotiana tabacum) that were grown under different environmental and nutritional conditions. The analysis of the measured hyperspectral signatures was done by artificial neural networks.