A Prediction Algorithm for Paddy Leaf Chlorophyll Using Colour Model Incorporate Multiple Linear Regression

Authors

  • Sattarpoom Thaiparnit King Mongkut's University of Technology North Bangkok
  • Mahasak Ketcham King Mongkut's University of Technology North Bangkok

DOI:

https://doi.org/10.4186/ej.2017.21.3.269

Keywords:

Chlorophyll estimation, image processing, regression analysis.

Abstract

This paper proposes the chlorophyll prediction in Pathumthani1 rice based on the image processing technique. The algorithm is developed to analyse the colour in the image by separating the components from rice leaf and computing the average value of red, green, and blue colours (RGB colours). The relationship between the average value and the amount of the chlorophyll is measured by using the chlorophyll meter SPAD-502 with multiple linear regressions. The results showed that the average value of the RGB colours is highly correlated with the amount of the chlorophyll from the rice leaf. To evaluate the accuracy, the chlorophyll prediction has been tested with 60 different rice leaves and the accuracy of the proposed method is 96.12%.

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Author Biographies

Sattarpoom Thaiparnit
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Department of Information Technology Management, King Mongkut's University of Technology North Bangkok, 1518 Pibulsongkram Road, Bangsue, Bangkok 10800, Thailand

Mahasak Ketcham
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Department of Information Technology Management, King Mongkut's University of Technology North Bangkok, 1518 Pibulsongkram Road, Bangsue, Bangkok 10800, Thailand

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Published In
Vol 21 No 3, Jun 15, 2017
How to Cite
[1]
S. Thaiparnit and M. Ketcham, “A Prediction Algorithm for Paddy Leaf Chlorophyll Using Colour Model Incorporate Multiple Linear Regression”, Eng. J., vol. 21, no. 3, pp. 269-280, Jun. 2017.
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