Evaluation of Color Models for Palm Oil Fresh Fruit Bunch Ripeness Classification

Sabri, Nurbaity; İbrahim, Zarina Bibi; Isa, Dino

doi:10.11591/ijeecs.v11.i2.pp549-557

Cited by 18 publications

(14 citation statements)

References 32 publications

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“…Non-optical methods usually measure the basic molecular content of all fruits such as moisture, which influences the overall performance of the system. The optical detection method can be further subdivided into 3 groups: imaging 5 , 17 – 21 , 21 – 26 , spectroscopy 6 , 27 – 30 and spectral imaging 31 – 35 . Among all the methods introduced earlier, imaging is the most popular method to date due to its portability, low cost, and ease of implementation.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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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“…In this approach, H stands for hue, which indicates color purity, S stands for saturation (the amount of white color incorporated in a certain color), and V stands for value. HSV color model is used in computer vision and image analysis for segmentation as illustrated in Figure 2 [21].…”

Section: Introductionmentioning

confidence: 99%

Traffic sign recognition based color model

Younus¹,

Al-Sabawi²

2022

IJEECS

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The main <span>problems that encounter the traffic light detection algorithm have become a handicap to the performance of the algorithms. Problems associated with the change of sign color due to bad weather and illumination changes of sunlight make the detection hard task. In the current work, we discuss these problems and propose a new idea of an efficient real time color sign recognition that relies only on color information. The proposed approach is based on building a red-model in hypothetical red, green, blue (RGB) cube using a large database of traffic signs. The segmentation has been implemented on the traffic signs that hold red color only as an example to illustrate the proposed approach. Results showed that the proposed algorithm is accurate as well as the computational cost is reduced.</span>

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“…Imaging techniques and computer vision are short of wellknown methods in determine the grading of oil palm [4][5][6][7][8]. Machine visioning through RGB decoding (Red, Green, Black) is also used to differentiate the type of oil palm [9][10][11][12][13]. Bensaeed et al [14] has utilized a hyperspectral device and machine learning algorithm to classify the various wavelength from the various types of oil palm fruit to grade the oil palm.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Four Terminal Fruit Battery With Charge Switching

Misron¹,

Ibrahim

Azhar

et al. 2021

IEEE Access

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Palm oil sector is considered one of main economical contributions in countries such a Malaysia where approximately 6.1% of their Gross Domestic Product (GDP) is contributed. Low Oil Extraction Rate (OER) from poor quality fresh fruit bunches have led to a decrease the oil palm production. The traditional method of using human naked eye to inspect the Fresh Fruit Bunches (FFB) during reception process is one of common methods that is still being practiced which eventually would lead to inaccuracy in grading and harvesting. This paper presents a new approach to identify the maturity of oil palm fruit using fruit-based maturity sensor and the electronic circuit. The study utilizes multiple terminals to evaluate the sensitivity of the sensor by analyzing the relation between the load voltage and the moisture content in the oil palm fruit. The sensitivity of the sensor increased by three-fold when four terminal fruit battery was used compared to single terminals with 47.61% for the moisture range of 50-80% value. This proposed approach significantly improved the accuracy in grading the oil palm fruit which is the most common challenge posed during the reception process.

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Evaluation of Color Models for Palm Oil Fresh Fruit Bunch Ripeness Classification

Cited by 18 publications

References 32 publications

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

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

Traffic sign recognition based color model

Implementation of Four Terminal Fruit Battery With Charge Switching

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