A Rapid and Non-Destructive Technique in Determining The Ripeness of Oil Palm Fresh Fruit Bunch (FFB)

Hashim, Fazida Hanim; Zulkifli, Z.; Raj, Thinal; Huddin, Aqilah Baseri

doi:10.17576/jkukm-2018-30(1)-12

Cited by 24 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Near infrared spectrometry (NIRS) is one of the nondestructive methods (Ismail, 2010), consisting of light detection and ranging (LiDAR) scanning systems that have been studied to classify oil palm ripeness in the Malaysian oil palm industry, where the proposed approach was based on calculating the reflectance percentage using the concept of linearity (Zulkifli et al, 2018). Another application classified oil palm ripening into under-ripe, ripe and over-ripe categories using hand-held, multi-parameter fluorescence sensors and blue-green (447 nm) and far-red (685 nm) wavelengths.…”

Section: Introductionmentioning

confidence: 99%

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

2023

ANRES

View full text Add to dashboard Cite

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.

show abstract

“…Classification methods such as k-Nearest Neighbor (KNN) were also used to compare feature values in each image according to the smallest differences from each study data [ 9 ]. However, due to the changes in light intensity throughout the day, images captured at different times may cause some discrepancies when determining the ripeness of oil palm fruits using the computer vision technique [ 10 ]. To overcome these problems, this study extends our previous work using Raman spectroscopy for oil palm fruit ripeness classification, due to its high potential in analyzing the biochemical content of a fruit sans light dependency [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Oil Palm Fruits Ripeness Classification Based on the Characteristics of Protein, Lipid, Carotene, and Guanine/Cytosine from the Raman Spectra

Tzuan

Hashim

Raj

et al. 2022

Plants

Self Cite

View full text Add to dashboard Cite

The capacity of palm oil production is directly affected by the ripeness of the fresh fruit bunches (FFB) upon harvesting. Conventional harvesting standards rely on rigid harvesting scheduling as well as the number of fruitlets that have loosened from the bunch. Harvesting is usually done every 10 to 14 days, and an FFB is deemed ready to be harvested if there are around 5 to 10 empty sockets on the fruit bunch. Technology aided by imaging techniques relies heavily on the color of the fruit bunch, which is highly dependent on the surrounding light intensities. In this study, Raman spectroscopy is used for ripeness classification of oil palm fruits, based on the molecular assignments extracted from the Raman bands between 1240 cm−1 and 1360 cm−1. The Raman spectra of 52 oil palm fruit samples which contain the fingerprints of different organic compounds were collected. Signal processing was applied to perform baseline correction and to reduce background noises. Characteristic data of the organic compounds were extracted through deconvolution and curve fitting processes. Subsequently, a correlation study between organic compounds was developed and eight hidden Raman peaks including protein, beta carotene, carotene, lipid, guanine/cytosine, chlorophyll-a, and tryptophan were successfully located. Through ANOVA statistical analysis, a total of six peak intensities from proteins through Amide III (β-sheet), beta-carotene, carotene, lipid, guanine/cytosine, and carotene and one peak location from lipid were found to be significant. An automated oil palm fruit ripeness classification system deployed with artificial neural network (ANN) using the seven signification features showed an overall performance of 97.9% accuracy. An efficient and accurate ripeness classification model which uses seven significant Raman peak features from the correlation analysis between organic compounds was successfully developed.

show abstract

A Rapid and Non-Destructive Technique in Determining The Ripeness of Oil Palm Fresh Fruit Bunch (FFB)

Cited by 24 publications

References 0 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

Oil Palm Fruits Ripeness Classification Based on the Characteristics of Protein, Lipid, Carotene, and Guanine/Cytosine from the Raman Spectra

Contact Info

Product

Resources

About