Abstract
High quality palm oil is critical in ensuring Malaysia’s competitiveness in the sector. Studies have shown that there is a significant relationship between the quality of palm oil produced and the ripeness of the fruits used in producing the oil. Correct ripeness of the fresh fruit bunches (FFB) produces higher quality and more oil content. Unripe FFB produces the least oil and overripe FFB produces oil of lower quality. According to Malaysian Palm Oil Board (MPOB), the main factors that determine the ripeness of the oil palm FFB are its colour and the number of its loose fruits. To classify the ripeness according to these two factors, there are 3 common techniques has been implemented in the previous work which are; colour feature extraction, texture feature extraction and Deep Learning method. To handle fruit ripeness classification problem, this paper provides a short review to the reader to grasp the applicable technique that can be implemented.
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