Oil Palm Fruit Maturity Classification Based on Texture Feature Extraction of Fruit Thorns and Supervised Machine Learning Classifiers Using Image Processing Technique

Alfatni, Meftah Salem M.; Shariff, Abdul Rashid Mohamed; Marhaban, Mohammad Hamiruce; Shafie, Suhaidi; Saaed, Osama M. Ben; Abdullah, Mohd Zaid; Amiruddin, Muhammad Majdy; Al-Mulla, Yaseen A.; Ahmed, Mahtab; Jayasuriya, H. P. W.

doi:10.17660/actahortic.2014.1054.42

Cited by 3 publications

(2 citation statements)

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“…Additionally, colour correction methods have been proposed to address inconsistent outdoor lighting. Haron et al (2012) (Alfatni et al, 2022;2018;2014b;Makky et al, 2013). Furthermore, outdoor images have proven to be more challenging, with texture feature extraction producing only 70% accuracy according to Ghazalli et al (2019), and 75% accuracy according to Ibrahim et al (2018).…”

Section: Feature Extractionmentioning

confidence: 99%

“…Limited studies on outdoor texture recognition Alfatni et al, 2022;2018;2014b;Ghazali et al, 2019;Ibrahim et al, 2018; In subsequent research, scholars have investigated the use of CNN-based algorithms for training labelled images and detecting FFB ripeness via sliding windows (Saleh et al, 2020). To enhance the system's efficiency, networks such as AlexNet (Wong et al, 2020), DenseNet (Herman et al, 2021), andResNet (Harsawardana et al, 2020;Khamis et al, 2022) have been implemented.…”

Section: Features Selectionmentioning

confidence: 99%

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A Picture of Ripeness: Investigating Image-Based Techniques for Oil Palm Fruit Grading

ROSBI

2024

JOPR

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Oil palm is a highly efficient crop that can produce more oil per unit of land than any other type of oil seed. Palm oil it is in high demand, and its production can significantly contribute to a country's economic growth. However, the traditional method of grading palm fruit is still prevalent in Malaysia, which requires skilled workers to classify the harvested fruit according to its ripeness. This approach can be costly and labour-intensive. Therefore, several studies have investigated automated palm fruit classification techniques that could reduce costs and labour in the industry. This paper provides a review of these studies, with a specific focus on vision-based classification techniques. The article discusses approaches based on image processing encompassing pre-processing, feature extraction and classification steps. The survey's resultsindicate that there is a lack of technique to effectively address outdoor images, such as colour correction methods. Therefore, further research is necessary to develop a better segmentation and colour correction procedures. Overall, the findings of this study could help improve the efficiency and sustainability of palm oil production, thereby contributing to economic growth and environmental conservation.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Features Selectionmentioning

confidence: 99%

A Picture of Ripeness: Investigating Image-Based Techniques for Oil Palm Fruit Grading

ROSBI

2024

JOPR

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Machine learning for automated oil palm fruit grading: The role of fuzzy C-means segmentation and textural features

Rosbi,

Omar,

Khairuddin

et al. 2024

Smart Agricultural Technology

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Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra

Raj

Hashim

Huddin

et al. 2021

Sci Rep

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The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm−1 is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits.

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Oil Palm Fruit Maturity Classification Based on Texture Feature Extraction of Fruit Thorns and Supervised Machine Learning Classifiers Using Image Processing Technique

Cited by 3 publications

References 0 publications

A Picture of Ripeness: Investigating Image-Based Techniques for Oil Palm Fruit Grading

A Picture of Ripeness: Investigating Image-Based Techniques for Oil Palm Fruit Grading

Machine learning for automated oil palm fruit grading: The role of fuzzy C-means segmentation and textural features

Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra

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