Oil palm leaf nutrient estimation by optical sensing techniques

• Main Authors: Khorramnia, Khosro, Khot, Lav R., Mohamed Shariff, Abdul Rashid, Ehsani, Reza, Mansor, Shattri, Abdul Rahim, Anuar
• Format: Article
• Published: American Society of Agricultural and Biological Engineers 2014
• Online Access: http://psasir.upm.edu.my/id/eprint/34716/
• Institution: Universiti Putra Malaysia

This study evaluated four optical sensor systems in an effort to characterize a robust nondestructive technique to estimate tree-specific oil palm leaflet nutrient deficiency and aid in determining the required fertilizer dosage rates. The four sensor systems used were a handheld reflectance sensor (GreenSeeker 505), a chlorophyll meter (SPAD 502 Plus), a fluorescence sensor (Multiplex 3), and a spectroradiometer (FieldSpec 3). Spectral responses were collected using the above sensors for leaf samples from 164 randomly selected trees from three commercial farms in Malaysia. The spectral data were processed using either stepwise multiple linear regression (SMLR) or rank features techniques for feature extraction. The feature datasets were analyzed using support vector machine (SVM), soft independent modeling of class analogies (SIMCA), and artificial neural network (ANN) classifiers. The SVM and ANN classifiers performed better compared to SIMCA for most nutrient prediction cases. Overall, with SMLR as a feature extraction technique and SVM as a classifier, a spectroradiometer can be used to effectively predict N, P, K, Mg, Ca, and B nutrient levels in oil palm leaflets. The average overall prediction accuracies for the above conditions were in the range of 59% to 85%. The spectroradiometer dataset was further analyzed using SVM regression models for estimating nutrient levels. Either SMLR-selected features or partial least square (PLS) latent variables were used for establishing the above regression models. The models fitted on the SMLR features performed better for N, Mg, Ca, and B, with correlation coefficients (r) for test datasets in the range of 0.45 to 0.78. The best fitted SVM regression models were for boron estimation, with a correlation coefficient of 0.78 and root mean squared error of prediction (RMSEP) of 2.82.