71cf539319367b8b5f12c9d 20241127114508906 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 11 30 2024 13 12 Department of Materials Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia. Nadia Adibah Rajab https://orcid.org/0009-0002-5471-2102 Dr. Nor Asmaa Alyaa Nor Azlan https://orcid.org/0000-0003-3317-1714 Department of Materials Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia. Prof. Dr. Wong Kuan Yew https://orcid.org/0000-0002-2944-6749 Department of Materials Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia. Prof. Dr. Adi Saptari https://orcid.org/0000-0001-9454-6511 Department of Industrial Engineering, President University, J1 KiHajar Dewantara, Kota Jababeka, Cikarang Baru, Bekasi. Prof. Dr. Effendi Mohamad https://orcid.org/0000-0003-3565-0575 Faculty of Industrial and Manufacturing Technology and Engineering, Universiti Teknikal Malaysia Melaka, Jalan Hang Tuah Jaya, Melaka, Malaysia. 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. 11 30 2024 11 30 2024 7 12 CC-BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) Based on my understanding, this article has no conflicts of interest. This article has not been funded by any organizations or agencies. This independence ensures that the research is conducted with objectivity and without any external influence. The content of this article does not necessitate ethical approval or consent to participate with supporting documentation. The adequate resources of this article are publicly accessible. The authorship of this article is contributed equally to all participating individuals. 10.35940/ijitee.K9994.13121124 https://www.ijitee.org/portfolio-item/K999413111024/