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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jin Wern Lai & Hafiz Rashidi Ramli & Luthffi Idzhar Ismail & Wan Zuha Wan Hasan, 2023. "Oil Palm Fresh Fruit Bunch Ripeness Detection Methods: A Systematic Review," Agriculture, MDPI, vol. 13(1), pages 1-16, January.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:1:p:156-:d:1028663
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    References listed on IDEAS

    as
    1. Nuzhat Khan & Mohamad Anuar Kamaruddin & Usman Ullah Sheikh & Yusri Yusup & Muhammad Paend Bakht, 2021. "Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps," Agriculture, MDPI, vol. 11(9), pages 1-26, August.
    2. Shahrzad Zolfagharnassab & Abdul Rashid Bin Mohamed Shariff & Reza Ehsani & Hawa Ze Jaafar & Ishak Bin Aris, 2022. "Classification of Oil Palm Fresh Fruit Bunches Based on Their Maturity Using Thermal Imaging Technique," Agriculture, MDPI, vol. 12(11), pages 1-20, October.
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