Automated detection of nitrogen status on plants: Performance of image processing techniques

Amin, S.R.M. and Awang, R. (2018) Automated detection of nitrogen status on plants: Performance of image processing techniques. In: 16th IEEE Student Conference on Research and Development, 26-28 Nov 2018, Selangor; Malaysia.

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Abstract

The significant role of nitrogen element in plants growth resulting in increased usage of nitrogen fertilizer in the agriculture field. With the aim to avoid improper use of nitrogen fertilization on plants and to assist local farmers in improving plants monitoring, this paper presents an economical and non-destructive method in determining nitrogen status of Napier grass using digital image processing approach. Three authentic techniques of image segmentation Otsu, K-means clustering, and watershed transformation were applied and compared to recognize the most accurate method for segmenting leaf pixel from its background. Otsu was discovered as the most efficient technique with less time-processing. Out of 36 features extracted from the segmented image, kurtosis, skewness and standard deviation of the blue color image were the most related features in classifying nitrogen status of the images. Classifiers like KNN, decision tree, and linear discriminant were used to classify the leaves image and nitrogen status accordingly. The accuracy of 100% was recorded in classifying the leaves image using decision tree and KNN classifier.

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