A rapid and non-destructive technique in determining the ripeness of oil palm fresh fruit bunch (FFB) - Zuhaira Mohd Zulkifli; Fazida Hanim Hashim; Thinal Raj; Aqilah Baseri Huddin (Penerbit Universiti Kebangsaan Malaysia, 2018)
Oil palm industry is one of the main industries in Malaysia that contributes to the country’s gross domestic product (GDP). In the oil palm industrial sector, methods of planting, detection and assessment are very important to produce high quality palm oil. Currently, the ripeness of oil palm fresh fruit bunch (FFB) is estimated using eyesight (most common), computer vision, hyperspectral imaging, light detection and ranging (LiDAR), near infrared (NIR) spectroscopy, and magnetic resonance imaging. The objective of this research is to introduce a rapid and non-destructive technique in determining and assessing the ripeness of oil palm fresh fruit bunch (FFB) by using a LiDAR scanning system. The LiDAR scanning system is used to scan three types of oil palm fruits at different level of ripeness which is under ripe, ripe, and over ripe. The reflectance intensity that bounces off the fruits are gathered and analysed to determine the different level or ripeness. Even though the intensity value is purely relative, it is proportional to the reflectance or absorption rate from the LiDAR sensor. A rapid method to determine the ripeness of palm fruits using a LiDAR sensor was proposed by calculating the reflectance percentage from 0% to 100% using the concept of linearity.