Abstract
Oil palm is one of the plantation commodities that has an important role in the economy of Indonesia. The high demand for palm oil in the future must be supported by high productivity. Fertilization is one of the intensification methods to increase oil palm productivity. The main nutrient content in oil palm that helps the growth and development of oil palm is nitrogen. Soil samples and location coordinates were taken from oil palm plantations then adjusted to Sentinel-1 satellite images. The Sentinel-1 image obtained is then processed first to reduce the factors that aggravate the process afterward. The digital number value obtained from the reflection of the Sentinel-1 image was then used as an independent variable and the soil nitrogen results from the laboratory become the dependent variable. Data were trained and a model was built using Random Forest Regressor (RFR) and Multi Linear Regression (MLR). All models built were evaluated by the quality of the model using MAPE and the best model was determined by selecting the lowest MAPE. RFR model has 19.53% of MAPE and MLR has 22.41% of MAPE. The RFR is chosen to be the best model based on the lowest MAPE and based on interpretation, the RFR model has good accuracy for determining soil nitrogen nutrient content better than MLR model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Badan Pusat Statistik: Statistik Kelapa Sawit Indonesia 2018 (2019)
Goh, K.J., Teo, C.B., Chew, P.S., Chiu, S.B.: Fertiliser Management in Oil Palm - Agronomic Principles and Field Practices (1998)
Safuan, L.O., Rembon, F.S., Syaf, H.: Evaluasi Status Hara Tanah dan Jaringan Sebagai Dasar Rekomendasi Pemupukan N,P, dan K pada Tanaman Kelapa Sawit. Agriplus. 23, 154–162 (2013)
Fageria, V.D.: Nutrient interactions in crop plants. J. Plant Nutr. 24, 1269–1290 (2001). https://doi.org/10.1081/PLN-100106981
Ge, Y., Thomasson, J.A., Sui, R.: Remote sensing of soil properties in precision agriculture: a review. Front. Earth Sci. (2011). https://doi.org/10.1007/s11707-011-0175-0
Muñoz-Huerta, R., Guevara-Gonzalez, R., Contreras-Medina, L., Torres-Pacheco, I., Prado-Olivarez, J., Ocampo-Velazquez, R.: A review of methods for sensing the nitrogen status in plants: advantages. Disadv. Recent Adv. Sens. 13, 10823–10843 (2013). https://doi.org/10.3390/s130810823
Shafri, H.Z.M., Hamdan, N., Izzuddin Anuar, M.: Detection of stressed oil palms from an airborne sensor using optimized spectral indices. Int. J. Remote Sens. 33, 4293–4311 (2012). https://doi.org/10.1080/01431161.2011.619208
Ahamed, T., Tian, L., Zhang, Y., Ting, K.C.: A review of remote sensing methods for biomass feedstock production. Biomass Bioenerg 35, 2455–2469 (2011). https://doi.org/10.1016/j.biombioe.2011.02.028
Lu, D.: The potential and challenge of remote sensing-based biomass estimation. Int. J. Remote Sens. 27, 1297–1328 (2006). https://doi.org/10.1080/01431160500486732
Lillesand, T.M., Kiefer, R.W.: Remote Sensing and Image Interpretation 4th Edition (1999)
Sutanto, A., Trisakti, B.: Perbandingan Klasifikasi Berbasis Obyek dan Klasifikasi Berbasis Piksel pada Data Citra Satelit Synthetic Aperture Radar untuk Pemetaan Lahan. 11, 13 (2014)
Tranmer, M., Murphy, J., Elliot, M., Pampaka, M.: Multiple Linear Regression (2nd Edition), p. 59 (2020)
Breiman, L.: Random forest. Mach. Learn. 45, 5–32 (2001)
Wang, L., Zhou, X., Zhu, X., Dong, Z., Guo, W.: Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. Crop J. 4, 212–219 (2016). https://doi.org/10.1016/j.cj.2016.01.008
Pahan, I.: Paduan Lengkap Kelapa Sawit. Penebar Swadaya, Jakarta (2007)
Barker, A.V., Pilbeam, D.J.: Handbook of Plant Nutrition. CRC PRess, Florida (2007)
Esetlili, M.T., Kurucu, Y.: Determination of main soil properties using synthetic aperture radar. Fresenius Environ. Bull. 25, 23–36 (2016)
Ulaby, F.T., Bush, T.F., Batlivala, P.P.: Radar response to vegetation II: 8-18 GHZ Band. IEEEE Trans. Antennas Propag. 23, 608–614 (1975)
Vincent, P., Bourbigot, M., Johnsen, H., Piantanida, R.: Sentinel-1 Product Specification (2020)
Sivasankar, T., Kumar, D., Srivastava, H.S., Patel, P.: Advances in radar remote sensing of agricultural crops: a review. Int. J. Adv. Sci. Eng. Inf. Technol. 8, 1126 (2018). https://doi.org/10.18517/ijaseit.8.4.5797
Lazecky, M., Lhota, S., Penaz, T., Klushina, D.: Application of Sentinel-1 satellite to identify oil palm plantations in Balikpapan Bay. IOP Conf. Ser. Earth Environ. Sci. 169, 012064 (2018). https://doi.org/10.1088/1755-1315/169/1/012064
Dalal, R.C., Henry, R.J.: Simultaneous determination of moisture, organic carbon, and total nitrogen by near infrared reflectance spectrophotometry. Soil Sci. Soc. Am. J. 50, 120–123 (1986). https://doi.org/10.2136/sssaj1986.03615995005000010023x
Morra, M.J., Hall, M.H., Freeborn, L.L.: Carbon and nitrogen analysis of soil fractions using near-infrared reflectance spectroscopy. Soil Sci. Soc. Am. J. 55, 288–291 (1991). https://doi.org/10.2136/sssaj1991.03615995005500010051x
Ehsani, M.R., Upadhyaya, S.K., Slaughter, D., Shafii, S., Pelletier, M.: A NIR technique for rapid determination of soil mineral nitrogen. Precis Agric. 1, 18 (1999)
Harfenmeister, K., Spengler, D., Weltzien, C.: Analyzing temporal and spatial characteristics of crop parameters using Sentinel-1 backscatter data. Remote Sens. 11, 1569 (2019). https://doi.org/10.3390/rs11131569
Kaliana, I.: Development of a decision support system for oil palm fertilizer requirement based on precision agriculture (2018)
Matloff, N.: Statistical Regression and Classification From Linear Models to Machine Learning. CRC Press, Davis(US) (2017)
Breusch, T.S., Pagan, A.R.: A simple test for heteroscedasticity and random coefficient variation. Econometrica 47, 1287 (1979). https://doi.org/10.2307/1911963
Dietterich, T.G., Arbib, M.A.: The Handbook of Brain Theory and Neural Network, 2nd edn. MIT Press, Cambridge (2002)
Liaw, A., Wiener, M.: Classification and Regression by random Forest, vol. 2, p. 6 (2002)
Montaño Moreno, J.J., Palmer Pol, A., Sesé Abad, A.: Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema 500–506 (2013). https://doi.org/10.7334/psicothema2013.23
Lewis, C.D.: Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting. Butterworth Scientific (1982)
Alpaydin, E.: Introduction to Machine Learning. MIT Press, Massachusetts (US) (2010)
Filipponi, F.: Sentinel-1 GRD preprocessing workflow. In: Proceedings, vol. 18, p. 11 (2019).https://doi.org/10.3390/ECRS-3-06201
Serikat Petani Kelapa Sawit: Dokumen SOP Agronomi Ini Untuk Petani Kalapa Sawit (2016)
Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E.B., Turaga, D.: Learning Feature Engineering for Classification. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 2529–2535. International Joint Conferences on Artificial Intelligence Organization, Melbourne, Australia (2017). https://doi.org/10.24963/ijcai.2017/352
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E.: Multivariate Data Analysis. Pearson Education Limited, London (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Budiman, R., Seminar, K.B., Sudradjat (2021). Development of Soil Nitrogen Estimation System in Oil Palm Land with Sentinel-1 Image Analysis Approach. In: Boumerdassi, S., Ghogho, M., Renault, É. (eds) Smart and Sustainable Agriculture. SSA 2021. Communications in Computer and Information Science, vol 1470. Springer, Cham. https://doi.org/10.1007/978-3-030-88259-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-88259-4_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88258-7
Online ISBN: 978-3-030-88259-4
eBook Packages: Computer ScienceComputer Science (R0)