Towards Sustainable Green Production: Exploring Automated Grading for Oil Palm Fresh Fruit Bunches (FFB) Using Machine Vision and Spectral Analysis

Abstract

Over the last decade, Indonesian palm oil industry has become a leading producer of the world, and been able to generatenotable foreign export reserves. In spite of this, problems still persist in this industry, including low productivity due to mishandling of raw material in post-harvest operations. One of the prime causes of this is manual grading/sorting of fresh fruit bunches, which is prone to error and misjudgement, as well as subjectivity. High demand of oil palm establishes its high price in world market, which drives the industry to expand its plantation area to increase production. Ultimately, it compromise forests and agricultural land, resulting stagnation or decline in several food products. Alternatively, before expanding plantation extent, oil extraction productivity of existing plantation can be improved by carefully selecting appropriate FFBs for post-harvest processing through introduction of automation. The use of machine vision and spectral analysis has shown to assist productivity of agricultural processing industry. This study employs automation technology for FFB grading in oil palm mills, resulting in improved raw material quality, thereby increasing the oil extraction productivity, and simultaneously contributing to partly release the pressure of deforestation by maintaining green agricultural areas.