Abstract

Image processing analysis for the soil deficiency identification has become an active area of research in this study. The changes in the color of the leaves are used to analyze and identify the deficiency of soil nutrients such as Nitrogen (N), Phosphorus (P) and potassium (K) by digital color image analysis. This research study focuses on the image analysis of the maize crop leaf using multivariate image analysis. In this proposed novel approach, initially, a color transformation for the input RGB image is formed and this RGB is converted to HSV because RGB is ideal for color generation but HSV is very suitable for color perception. Then green pixels are masked and removed using specific threshold value by applying histogram equalization. This masking approach is done through specific customized filtering approach which exclusively filters the green color of the leaf. After the filtering step, only the deficiency part of the leaf is taken for consideration. Then, a histogram generation is carried out for the deficiency part of the leaf. Then, Multivariate Image Analysis approach using Independent Component Analysis (ICA) is carried out to extract a reference eigenspace from a matrix built by unfolding color data from the deficiency part. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute nutrient deficiency based on the T2 statistic. In addition, a multi-resolution scheme by scaling down process is carried out to speed up the process. Finally, based on the training samples, the soil deficiency is identified based on the color of the maize crop leaf.

Keywords:

Histogram equalization, independent component analysis, multivariate image analysis, nutrient deficiency, unsupervised approach,

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