Towards a Real-Time Oil Palm Fruit Maturity System Using Supervised Classifiers Based on Feature Analysis

Meftah Salem M. Alfatni, Siti Khairunniza-Bejo (), Mohammad Hamiruce B. Marhaban, Osama M. Ben Saaed, Aouache Mustapha and Abdul Rashid Mohamed Shariff
Additional contact information
Meftah Salem M. Alfatni: Libyan Authority for Scientific Research, Tripoli P.O. Box 80045, Libya
Siti Khairunniza-Bejo: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Mohammad Hamiruce B. Marhaban: Centre for Control System and Signal Processing, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Osama M. Ben Saaed: Libyan Authority for Scientific Research, Tripoli P.O. Box 80045, Libya
Aouache Mustapha: Division Télécom, Centre de Développement des Technologies Avancées, CDTA, Baba-Hassen 16303, Algeria
Abdul Rashid Mohamed Shariff: Department of Biological and Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia

Agriculture, 2022, vol. 12, issue 9, 1-28

Abstract: Remote sensing sensors-based image processing techniques have been widely applied in non-destructive quality inspection systems of agricultural crops. Image processing and analysis were performed with computer vision and external grading systems by general and standard steps, such as image acquisition, pre-processing and segmentation, extraction and classification of image characteristics. This paper describes the design and implementation of a real-time fresh fruit bunch (FFB) maturity classification system for palm oil based on unrestricted remote sensing (CCD camera sensor) and image processing techniques using five multivariate techniques (statistics, histograms, Gabor wavelets, GLCM and BGLAM) to extract fruit image characteristics and incorporate information on palm oil species classification FFB and maturity testing. To optimize the proposed solution in terms of performance reporting and processing time, supervised classifiers, such as support vector machine (SVM), K-nearest neighbor (KNN) and artificial neural network (ANN), were performed and evaluated via ROC and AUC measurements. The experimental results showed that the FFB classification system of non-destructive palm oil maturation in real time provided a significant result. Although the SVM classifier is generally a robust classifier, ANN has better performance due to the natural noise of the data. The highest precision was obtained on the basis of the ANN and BGLAM algorithms applied to the texture of the fruit. In particular, the robust image processing algorithm based on BGLAM feature extraction technology and the ANN classifier largely provided a high AUC test accuracy of over 93% and an image-processing time of 0,44 (s) for the detection of FFB palm oil species.

Keywords: external grading system; oil palm FFB; machine learning; supervised classifiers; quality inspection; remote sensing (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: 2022
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