Novel Feature Extraction for Oil Palm Bunches Classification

• Main Authors: Wang, Hui Hui, Wang, Yin Chai, Wee, Bui Lin, Sim, Shiang Wei
• Format: Article
• Language: English
• Published: Semarak Ilmu Publishing 2024
• Subjects: QA75 Electronic computers. Computer science; T Technology (General)
• Online Access: http://ir.unimas.my/id/eprint/44468/1/Novel%20Feature.pdf; http://ir.unimas.my/id/eprint/44468/; https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/3348; https://doi.org/10.37934/araset.34.1.350360
• Institution: Universiti Malaysia Sarawak

This paper presents research on an image processing approach for oil palm fruit brunches classification on automating the process of classifying palm fruit brunches based on their visual characteristics, which can have applications in the agriculture and palm oil industry. The research aims to classify the bunches into four categories which are unripe, under ripe, ripe and over ripe. Additionally, this approach can reduce the manual labour involved in palm oil grading and provide a more objective and consistent method for grading the oil. The proposed approach consists of the process of image acquisition, image pre-processing, colour processing, image segmentation and classification. The ripeness of the oil palm fruit bunches is determined based on the percentage of ripeness areas masked on the fruit surface. The proposed algorithms successfully classified the palm oil bunches and improved the accuracy of grading palm oil, achieving 85% accuracy from the experiment results