Distribution map of oil palm fresh fruit bunch using LiDAR*

Abstract

Oil palm tree has been the key to Malaysia’s economic expansion, where it has become the most important, national agricultural crop. Techniques of planting, assessment, and detection are crucial to harvest a good quality of palm oil. At present, the ripeness of oil palm fresh fruit bunch (FFB) is determined using computer vision, eyesight, near-infrared (NIR) spectroscopy, light detection and ranging (LiDAR), and Hue, Saturation, and Intensity (HSI) techniques. This research objective is to categorize the ripeness of oil palm FFB from data received from the sensor and to create a distribution map. The methodology chosen to develop this project is the waterfall model, while the findings of this study will be the classified ripeness of oil palm FFB are either under-ripe, ripe, or overripe, and the map of distribution.