Ripeness detection simulation of oil palm fruit bunches using laser-based imaging system

Shiddiq, Minarni; Fitmawati,; Anjasmara, Ridho; Sari, Nurmaya; Hefniati,

doi:10.1063/1.4973101

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…However, the low coherence property of broadband light makes it less ideal for remote sensing/measurement. Multi-band assessment techniques that are based on the LED sources at specific wavelengths have been suggested [11,12]. The work distance is limited (< 1m) and technically impractical for on-site operation and on-plant assessment.…”

Section: Introductionmentioning

confidence: 99%

Laser remote sensor for oil palm fruit ripeness assessment

Lim,

Nazri,

Rusik

et al. 2024

Photonic Technologies in Plant and Agricultural Science

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The color change of oil palm fresh fruit bunches (FFB) is one of the important parameters for harvesting decisions. However, human visual identification is prone to errors due to uncontrolled ambient lighting and the far distance of fruit bunches on tall palm trees. These errors can lead to inaccurate harvesting and significant revenue loss. This study introduces a laser remote sensor for non-destructive FFB ripeness assessment. Based on the unique spectral reflection curves of the FFB at different ripeness, laser modules at three different wavelengths (visible-NIR region) have been employed for the measurement. The photodetector and laser sources are configured in a coaxial manner to enable long work distances up to 9m. This portable laser remote sensor has undergone successful on-plant testing in oil palm estates, with measurements validated against oil content by conventional bunch analysis. It is a potential tool for precision harvesting and oil yield prediction.

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Section: Introductionmentioning

confidence: 99%

Laser remote sensor for oil palm fruit ripeness assessment

Lim,

Nazri,

Rusik

et al. 2024

Photonic Technologies in Plant and Agricultural Science

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“…A laser-based imaging system (called hyperspectral imaging) with two diode lasers was used to examine the reflectance due to interaction with an OPFFB pigment of anthocyanin and chlorophyll contents (Shiddiq et al, 2017). Techniques using color-based machine vision linked to computer applications used the color intensity to differentiate OPFFB ripeness categories (Abdullah et al, 2002), with further application to classify the ripeness based on the color and texture successfully achieving an accuracy of 98.3% (Septiarini et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Real-time oil palm ripeness classification of fresh fruit bunches using fluorescence technology

2023

ANRES

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Importance of the work:The precise and non-destructive grading of oil palm fresh fruit bunches (OPFFBs) is crucial in determining the profit for farmers selling the product to factories. Objectives: To investigate the accuracy of fluorescence technology combined with image analysis technology in categorizing OPFFB ripeness. Materials & Methods: A combination of eight ultraviolet light-emitting diodes (UV LEDs) were used in the wavelength range 395-400 nm along with image processing techniques to classify oil palm ripening in the Tenera variety (80 ripe and 20 under-ripe bunches). Afterward, the UV LEDs stimulated the OPFFBs in darkness, as indicated by fluorescence in the image processing. To validate the results, the fluorescence visibility results were compared with the results manually provided by five experts and with crude palm oil percentages obtained from 600 fruits samples extracted using an organic solvent (six fruits were sampled from each bunch). Results: The equipment yielded results that were deemed 100% accurate, as confirmed by experts and the crude palm oil percentages obtained from laboratory analysis. Main finding:The research equipment demonstrated enhanced fluorescence visibility for assessing the maturity of Tenera variety oil palm fresh fruit bunches. This technique may have potential applications in assessing the maturity of other oil palm varieties, as well as different types of fruit.

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“…Besides, NDM do not rupture the fruit tissue and can be used to assess the internal variables of fruits. These include applications of LiDAR scanning [11], optical-based sensors [12], computer and camera vision system [13], laser-based imaging system [14], handheld optical spectrometer [15], LED optical sensor [16], thermal imaging technique [17], and fruit battery [18]. Table 2 gives a summary of the applications of different NDM to classify FFB maturity levels.…”

Section: Introductionmentioning

confidence: 99%

Application of Optical Spectrometer to Determine Maturity Level of Oil Palm Fresh Fruit Bunches Based on Analysis of the Front Equatorial, Front Basil, Back Equatorial, Back Basil and Apical Parts of the Oil Palm Bunches

2021

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The quality of palm oil depends on the maturity level of the oil palm fresh fruit bunch (FFB). This research applied an optical spectrometer to collect the reflectance data of 96 FFB from unripe, ripe, and overripe classes for the maturity level classification. The spectrometer scanned the FFB from different parts, including apical, front equatorial, front basil, back equatorial, and back basil. Principal component analysis was carried out to extract principal components from the reflectance data of each of the parts. The extracted principal components were used in an ANOVA test, which found that the reflectance data of the front equatorial showed statistically significant differences between the three maturity groups. Then, the collected reflectance data was subjected to machine learning training and testing by using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The front equatorial achieved the highest accuracy, of 90.6%, by using SVM as classifiers; thus, it was proven to be the most optimal part of FFB that can be utilized for maturity classification. Next, the front equatorial dataset was divided into UV (180–400 nm), blue (450–490 nm), green (500–570 nm), red (630–700 nm), and NIR (800–1100 nm) regions for classification testing. The UV bands showed a 91.7% accuracy. After this, representative bands of 365, 460, 523, 590, 623, 660, 735, and 850 nm were extracted from the front equatorial dataset for further classification testing. The 660 nm band achieved an 89.6% accuracy using KNN as a classifier. Composite models were built from the representative bands. The combination of 365, 460, 735, and 850 nm had the highest accuracy in this research, which was 93.8% with the use of SVM. In conclusion, these research findings showed that the front equatorial has the better ability for maturity classification, whereas the composite model with only four bands has the best accuracy. These findings are useful to the industry for future oil palm FFB classification research.

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Ripeness detection simulation of oil palm fruit bunches using laser-based imaging system

Cited by 13 publications

References 12 publications

Laser remote sensor for oil palm fruit ripeness assessment

Laser remote sensor for oil palm fruit ripeness assessment

Real-time oil palm ripeness classification of fresh fruit bunches using fluorescence technology

Application of Optical Spectrometer to Determine Maturity Level of Oil Palm Fresh Fruit Bunches Based on Analysis of the Front Equatorial, Front Basil, Back Equatorial, Back Basil and Apical Parts of the Oil Palm Bunches

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