Research Article | OPEN ACCESS
Analysis of Maize Crop Leaf using Multivariate Image Analysis for Identifying Soil Deficiency
S. Sridevy and Anna Saro Vijendran
1Depertment of PS and IT, AEC and RI, Tamil Nadu Agricultural University
2MCA in SNR Sons College, Coimbatore, Tamil Nadu, India
Research Journal of Applied Sciences, Engineering and Technology 2014 19:2071-2081
Received: May 19, 2014 | Accepted: July 07, 2014 | Published: November 20, 2014
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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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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The authors have no competing interests.
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