NLCS - A novel coordinate system for spatial analysis on hyperspectral leaf images and an improved nitrogen index for soybean plants

Abstract

Hyperspectral imaging (HSI) has been increasingly applied in plant phenotyping projects. However, most HSI systems’ imaging quality is compromised by various noise factors such as the changing ambient light, leaf slopes, and so on. In recent years, new HSI devices such as LeafSpec have been introduced to provide a higher signal-over-noise ratio along with higher spectral and spatial resolutions. However, most of the previous image processing software only calculated the averaged spectrum over the whole leaf, but rarely include the spatial distribution analysis on the leaf level. Meanwhile, different nutrient stresses could result in different spatial distribution patterns on the leaf which can be used to elevate the quality of plant phenotyping. This study focused on the development of a new methodology for spatial distribution analysis of the leaf-level HSI images. Firstly, a novel way of encoding the soybean leaf pixels to a new coordinate system called the Natural Leaf Coordinate System (NLCS) was introduced. NLCS defined the coordinates of every leaf pixel relative to the venation structure of the leaf so that the spatial distribution analysis could be conducted more intuitively. Second, a new nitrogen index based on NLCS called NLCS-N was developed and able to outperform the whole leaf averaged NDVI in terms of predicting the nitrogen content of the soybean plants and distinguishing the nitrogen-sufficient plants from the nitrogen-deficient ones more significantly.