A Cutting-Edge Precision Agriculture Technology to Support the Sustainable Oil Palm Industry

Abstract

Abstract One of the most important factors in attaining sustainability in oil palm plantations is proper production input management in accordance with Good Agronomic Practices. For controlling plant disease and fertilizing, it can be started with an accurate monitoring technique to identify disease infection and the level of leaf nutrients in the field. The monitoring method should also be inexpensive, rapid, less time-consuming, and repeatable. This study has demonstrated how image classification (remote sensing) can be used to locate oil palm trees that have the Basal Stem Rot (BSR) disease and to estimate the nutritional level of the leaves. The healthy and BSR-infected palms had been effectively recognized and mapped using the remote sensing approach, which was used in conjunction with machine learning as well as a multispectral camera from a satellite and UAV. Furthermore, the use of a UAV and Mapir camera had resulted in a good prediction of N, P, K, and Mg content in the palm leaves; therefore, it may be practical to monitor leaf nutrient status in the oil palm plantations.