Oil Palm Fresh Fruit Bunch Ripeness Detection Methods: A Systematic Review

Jin Wern Lai, Hafiz Rashidi Ramli (), Luthffi Idzhar Ismail and Wan Zuha Wan Hasan
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Jin Wern Lai: Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Hafiz Rashidi Ramli: Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Luthffi Idzhar Ismail: Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
Wan Zuha Wan Hasan: Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia

Agriculture, 2023, vol. 13, issue 1, 1-16

Abstract: The increasing severity of the labour shortage problem in the Malaysian palm oil industry has created a need to explore other avenues for harvesting oil palm fresh fruit bunches (FFBs) such as through autonomous robots’ deployment. However, the first step in using an autonomous system to harvest FFBs is to identify which FFBs have become ripe and are ready to be harvested. In this work, we reviewed previous and current methods of identifying the maturity of fresh fruit bunches as found in the literature. The different methods were then compared in terms of the types of sample data used, sensor modalities, and types of classifiers used with a particular focus on the feasibility of each method for on-field application. From the 51 papers reviewed, which include a total of 11 unique approaches, it was found that the most feasible method for detecting ripe FFBs in the field is a combination of computer vision and deep learning. This system has the advantages of being a noncontact approach that is low cost while also being able to operate in real time with high accuracy.

Keywords: oil palm; fresh fruit bunch; maturity; ripeness; detection; grading (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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