Prediction of SPAD chlorophyll meter readings using remote sensing technique

C.C. Teoh, D. Abu Hassan, M. Muhammad Radzali and J.J. Jafni

Abstract

A method using unmanned airborne vehicle (UAV) and image processing technique to enable prediction of SPAD chlorophyll meter readings was developed. Relationships between SPAD readings and R, G, B, R/(R+G+B), G/(R+G+B), and B/(R+G+B) values were analysed. The R/(R+ +B) values indicate the highest correlation with SPAD readings with r2 value of –0.9695 and a SPAD reading prediction model was developed from the relationship analysis. The prediction model is capable to predict SPAD reading with average accuracy value of 89%. A SPAD reading map was generated by converting the spectral reflectance values into SPAD readings using the prediction model. This SPAD reading map was classified into high, medium and low levels of SPAD values for easy identification of N stress levels in the paddy fields.

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