Published November 30, 2024 | Version CC-BY-NC-ND 4.0
Journal article Open

A Comprehensive Methodology for Image Recognition Utilizing Machine Learning and Computer Vision: Automation of the Harvesting Process

Creators

  • 1. Department of Materials Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.

Contributors

  • 1. Department of Materials Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
  • 2. Department of Industrial Engineering, President University, J1 KiHajar Dewantara, Kota Jababeka, Cikarang Baru, Bekasi.
  • 3. Faculty of Industrial and Manufacturing Technology and Engineering, Universiti Teknikal Malaysia Melaka, Jalan Hang Tuah Jaya, Melaka, Malaysia.

Description

Abstract: This study aims to investigate the machine learning techniques implemented in image recognition technology for the identification and classification of oil palm fruit ripeness. The accurate determination of fruit ripeness is crucial for optimizing harvest time and improving oil yield. The palm oil industry is one of the major plantations in Malaysia. The harvesting process of oil palm fruit was conducted with traditional methods by relying on manual inspection, which can be subjective and inconsistent. Plus, it required several workers. A model of image recognition was developed using machine learning algorithms and computer vision to automate the harvesting process and overcome the shortage of labor issues. Implementing this technology in the field could lead to more consistent harvests and higher-quality oil production. Several machine learning models were developed, trained, and tested for their ability to classify the ripeness stages. The findings suggest the trending techniques in implementing image recognition which can provide a reliable and efficient tool for assessing oil palm fruit ripeness.

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Additional details

Identifiers

DOI
10.35940/ijitee.K9994.13121124
EISSN
2278-3075

Dates

Accepted
2024-11-15
Manuscript received on 27 September 2024 | Revised Manuscript received on 06 October 2024 | Manuscript Accepted on 15 November 2024 | Manuscript published on 30 November 2024.

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