Prof. T. Sabrina Djunita Profile

Prof. T. Sabrina Djunita

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Proceedings Article | 29 January 2024 Paper

Mapping the nutrient content of oil palm leaves based on machine learning to determine fertilizer recommendations in North Sumatra

Dhimas Wiratmoko, T. Sabrina Djunita, Budiman Minasny, Zulkifli Nasution, Abdur Rauf, Retnadi Heru Jatmiko

Proceedings Volume 12977, 129770K (2024) https://doi.org/10.1117/12.3009700

KEYWORDS: Machine learning, Landsat, Earth observing sensors, Potassium, Nitrogen, Magnesium, Remote sensing, Phosphorus, Agriculture, Random forests

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Oil palm is an essential commodity for Indonesia, which generated USD 28.72 billion in foreign exchange in 2021. This commodity has a history for North Sumatra since the beginning of the oil palm industry, where the origins of the palm oil industry were in the province of North Sumatra. The importance of oil palm for the economy in North Sumatra can be seen from the production of palm oil in the region which fourth ranks in the national palm oil production. The success of oil palm cultivation is strongly influenced by various production factors, one of which is fertilization activities to replace the lost nutrients through harvest or other activities. Accuracy in fertilizing activities is the primary key to the success of oil palm production. Determining the fertilization dosage for oil palm plants currently requires high costs and a relatively long time because it requires leaf analysis in the laboratory. Mapping oil palm leaf nutrients through satellite imagery, especially Landsat-8 imagery, is one of the non-destructive alternative steps to determine the nutrient content of oil palm leaves quickly and precisely. This study aims to map and classify the nutrient condition of oil palm leaves as a reference for preparing the correct dosage of fertilization recommendations in the North Sumatra region. The methods used in this study are three types of classification using machine learning, namely classification and regression tree (CART), random forest (RF), and support vector machine (SVM). The classification results of the three types of machine learning have a high accuracy in classifying or mapping oil palm leaf nutrients in North Sumatra, which is then followed by calculating doses based on plant-transported nutrients and nutrient availability in oil palm leaves. Based on this, the three-machine learning have the potential to provide information quickly on the nutrient content of oil palm leaves.

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