[U-04] Geospatial Applications for Societal Benefits

[U04-06] From Color to Oil Content: Smart Technologies for Oil Palm Maturity Grading

*Abdul Rashid Bin Mohamed Shariff ¹, Shahrzad Zolfagharnassab¹, Meftah salem M. Alfatni², Mohd Hafiz Mohd Hazir³, Osama Mohammed Ben Saeed² (1.Universiti Putra Malaysia , 2.Libyan Authority for Scientific Research, 3.Malaysian Rubber Board)

Keywords:Oil palm, Fresh fruit bunch maturity, Oil quality parameters, Oil content, Image analysis, Thermal scanning

Oil palm maturity grading plays a crucial role in determining oil yield and quality, traditionally relying on manual inspection methods that are labor-intensive, prone to error, and biased towards mills. This study explores alternative, technology-driven approaches to grading oil palm fresh fruit bunches (FFB) based on oil content and quality parameters. Utilizing digital imaging, hyperspectral analysis, and thermal imaging, the research establishes correlations between fruit maturity stages—under-ripe, ripe, and overripe—and oil quality indices such as Free Fatty Acid (FFA), Peroxide Value (PV), Deterioration of Bleachability Index (DOBI), and carotene content. Digital image processing revealed that red, green, and blue color bands correlate differently with oil content at various maturity levels. Thermal imaging demonstrated that immature fruit exhibits higher temperatures, which decreases as maturity progresses. An Artificial Neural Network (ANN) classifier was employed, achieving high accuracy in predicting maturity (88%) and oil content (94.6%). These findings contribute to the development of automated, real-time maturity grading systems, enhancing efficiency and consistency in oil palm fruit classification and oil quality assessment.