The Exploration of The Oil Palm Leaf Nutrients Content Using Unmanned Aerial Vehicle and Multispectral Camera

Main Article Content

Heri Santoso
Winarna Winarna


Fertilizer recommendation is yearly activities for oil palm nutrient requirement correction by fertilization activities. Oil palm leaf nutrients content in fertilization recommendation activities is a small part of several parameters used. In a row of the remote sensing gains, the researchers have been studying the oil palm leaf nutrients content prediction from multispectral satellite data and field spectral from spectroradiometer and they have a variance of results. According to the existing research of the oil palm leaf nutrients content prediction, the unmanned aerial vehicle (UAV) assembled by the multispectral camera does not yet use for oil palm leaf nutrients. Therefore, this research is necessary to do. The objectives of this research were to compare the model predictor of oil palm leaf nutrient contents with several variations of variables from three bands of the multispectral camera and several vegetation indices and also to determine the best model predictors from them. The image from the multispectral camera of Mapir Survey 3 that consists of green, red, and near-infrared, and also simple ratio, normalized difference vegetation index, as well as green NDVI were used as the independent variable of regression analysis included simple regression, polynomial regression, multivariant regression with selected variables from linear regression and random forest methods of recursive feature elimination technic, and polynomial multivariant regression. The responses were the leaf nutrient analysis of N, P, K, Ca, Mg, and B from twenty samples. The results showed the best model predictor was the regression model from polynomial multivariant regression with variables from RF method of RFE technique. The model just predicted the N, P, K, and Mg oil palm leaf nutrients with 0.9415 to 0.9991 R2 value, 0.7223 to 0.9837 adjusted R2 value, and 0.0045 to 0.0340 residual standard error value (RSE).


Download data is not yet available.

Article Details

How to Cite
Santoso, H., & Winarna, W. (2021). The Exploration of The Oil Palm Leaf Nutrients Content Using Unmanned Aerial Vehicle and Multispectral Camera. Jurnal Penelitian Kelapa Sawit, 29(1), 49-62.


Cheng, C. L., Shalabh, & Garg, G. (2014). Coefficient of determination for multiple measurement error models. Journal of Multivariate Analysis, 126, 137–152.
Corley, R. H. V, & Tinker, P. B. (2003). The Oil Palm. In Blackwell Science Ltd (Fourth Edi). Blackwell Science Ltd,.
Coster, A. (2021). Goodness-of-Fit Statistics. Web.Maths.Unsw.Edu.Au/~adelle/.
Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298.
Goh, K. J. (2004). Fertilizer Recommendation Systems for Oil Palm : Estimating the Fertilizer Rates. Proceedings of MOSTA Best Practices Workshops: Agronomy and Crop Management, Malaysia, March to August 2004. Sdn, 235–268. recommendation systems for oil palm - estimating the fertilizer rates.pdf
Gromski, P. S., Xu, Y., Correa, E., Ellis, D. I., Turner, M. L., & Goodacre, R. (2014). A comparative investigation of modern feature selection and classification approaches for the analysis of mass spectrometry data. Analytica Chimica Acta, 829, 1–8.
Heiri, O., & Lotter, a. F. (2010). How does taxonomic resolution affect chironomid-based temperature reconstruction? Journal of Paleolimnology, 44, 589–601.
Kalaitzidis, C; Heinzel, V; Zianis, D. (2010). A review of vegetation indices for the estimation of biomass.pdf. Imagin[e,g] Europe : Proceedings of the 29th Symposium of the European Association of Remote Sensing Laboratories, Chania, Greece, January 2016, 8.
Kuhn, M. (2015). Caret. The Comprehensive R Archive Network (CRAN).
Marzukhi, F., Elahami, A. L., & Bohari, S. N. (2016). Detecting nutrients deficiencies of oil palm trees using remotely sensed data. IOP Conference Series: Earth and Environmental Science, 37(1).
Moraes, D. (2012). Letters The Coefficient of Determination : What. Investigative Ophtalmology & Visual Science, 53(11), 6830–6832.
Özyigit, Y., & Bilgen, M. (2013). Use of spectral reflectance values for determining nitrogen, phosphorus, and potassium contents of rangeland plants. Journal of Agricultural Science and Technology, 15(SUPPL), 1537–1545.
Pimstein, A., Karnieli, A., Bansal, S. K., & Bonfil, D. J. (2011). Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy. Field Crops Research, 121(1), 125–135.
Prabowo, N. E., Foster, H. L., Nelson, S., & Nelson, P. (2010). Practical use of oil palm nutrient physiological efficiency with regard to nutrient recovery and agronomic efficienceies at different Sumatran sites. XVII Th International Oil Palm Conference , Colombia, 2005, 1–34.
Priyanka Sinha. (2013). Multivariate Polynomial Regression in Data Mining: Methodology, Problems and Solutions. International Journal of Scientific & Engineering Research, 4(12), 962–965.
R Core Team. (2016). R: A language and environment for statistical computing (3.3.1). R Foundation for Statistical Computing.
Rendana, M., Abdul, S., Mohd, W., Idris, R., Lihan, T., & Ali, Z. (2015). A Review of Methods for Detecting Nutrient Stress of Oil Palm ( Elaeis guineensis Jacq .) in Malaysia A Review of Methods for Detecting Nutrient Stress of Oil Palm ( Elaeis guineensis Jacq .) in Malaysia. 5(6), 60–64.
RStudio. (2015). Integrated development environment (IDE) for R Code (0.99.489). RStudio, Inc.
Santoso, H. (2020). Pengamatan dan Pemetaan Penyakit Busuk Pangkal Batang di Perkebunan Kelapa Sawit Menggunakan Unmanned Aerial Vehicle ( UAV ) dan Kamera Multispektral. Jurnal Fitopatologi Indonesia, 16(2), 69–80.–80
Santoso, H., Tani, H., Wang, X., Prasetyo, A. E., & Sonobe, R. (2019). Classifying the severity of basal stem rot disease in oil palm plantations using WorldView-3 imagery and machine learning algorithms. International Journal of Remote Sensing, 40(19: Oil Palms), 1–23.
Santoso, H., Tani, H., Wang, X., & Segah, H. (2019). Predicting oil palm leaf nutrient contents in kalimantan, indonesia by measuring reflectance with a spectroradiometer. International Journal of Remote Sensing, 40(19: Oil Palms), 1–22.
Shafri, H. Z. M., Hamdan, N., & Izzuddin Anuar, M. (2012). Detection of stressed oil palms from an airborne sensor using optimized spectral indices. International Journal of Remote Sensing, 33(14), 4293–4311.
Spiess, A.-N., & Neumeyer, N. (2010). An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach. BMC Pharmacology, 10, 6.
Stein, B. R., Thomas, V. a., Lorentz, L. J., & Strahm, B. D. (2014). Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales. GIScience & Remote Sensing, 51(3), 269–287.
Sulaeman, Suparto, & Eviati. (2005). Analisis Kimia Tanah, Tanaman, Air, Dan Pupuk (F. Agus (ed.); 2nd ed.). Indonesian Soil Research Institute.
Wei, J., Chen, T., Liu, G., & Yang, J. (2016). Higher-order Multivariable Polynomial Regression to Estimate Human Affective States. Scientific Reports, 6(March), 1–13.
Witt, C., Fairhurst, T., & Griffiths, W. (2005). Key Principles of Crop and Nutrient Management in Oil Palm. Better Crops, 89(3), 27–31.$webindex/DF49AA8010785C14852570490074C097/$file/05-3p27.pdf
Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017.
Yadegari, M., Shamshiri, R. R., Shariff, A. R. M., Balasundram, S. K., & Mahns, B. (2020). Using spot-7 for nitrogen fertilizer management in oil palm. Agriculture (Switzerland), 10(4).
Zhai, Y., Cui, L., Zhou, X., Gao, Y., Fei, T., & Gao, W. (2013). Estimation of nitrogen, phosphorus, and potassium contents in the leaves of different plants using laboratory-based visible and near-infrared reflectance spectroscopy: Comparison of partial least-square regression and support vector machine regression met. International Journal of Remote Sensing, 34(7), 2502–2518.

Most read articles by the same author(s)