Real-time oil palm ripeness classification of fresh fruit bunches using fluorescence technology

Abstract

Importance of the work: The precise and non-destructive grading of oil palm fresh fruit bunches (OPFFBs) is crucial in determining the profit for farmers selling the product to factories.
Objectives: To investigate the accuracy of fluorescence technology combined with image analysis technology in categorizing OPFFB ripeness.
Materials & Methods: A combination of eight ultraviolet light-emitting diodes (UV LEDs) were used in the wavelength range 395–400 nm along with image processing techniques to classify oil palm ripening in the Tenera variety (80 ripe and 20 under-ripe bunches). Afterward, the UV LEDs stimulated the OPFFBs in darkness, as indicated by fluorescence in the image processing. To validate the results, the fluorescence visibility results were compared with the results manually provided by five experts and with crude palm oil percentages obtained from 600 fruits samples extracted using an organic solvent (six fruits were sampled from each bunch).
Results: The equipment yielded results that were deemed 100% accurate, as confirmed by experts and the crude palm oil percentages obtained from laboratory analysis.
Main finding: The research equipment demonstrated enhanced fluorescence visibility for assessing the maturity of Tenera variety oil palm fresh fruit bunches. This technique may have potential applications in assessing the maturity of other oil palm varieties, as well as
different types of fruit.