Non-Destructive Oil Palm Fresh Fruit Bunch (FFB) Grading Technique Using Optical Sensor

Utom, Sebastian Liban; Mohamad, Elmy Johana; Ameran, Hanis Liyana Mohmad; Kadir, Herdawatie Abdul; Muji, Siti Zarina Mohd; Rahim, Ruzairi Abdul; Pusppanathan, Jaysuman

doi:10.30880/ijie.2018.10.01.006

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…The visible-infrared spectrum has higher intensities in the region of 700-900 nm, the same fashion found in similar research of hyperspectral and infrared imaging on oil palm fresh fruit bunches [25], [27]. The measurement using four-band optical sensors showed higher reflectance intensities in the infrared region (700-900 nm) due to fewer absorbances by chlorophyll and anthocyanin in the mesocarp layer [22].…”

Section:  Issn: 1693-6930supporting

confidence: 80%

“…Electronic sensors and imaging techniques were capable to predict the ripeness levels of oil palm FFBs and improve the sorting and grading processes. A detection system has used a 670 nm light source and an optical sensor to determine the FFB ripeness levels [22]. Moreover, imaging techniques such as thermal imaging [23], laser-induced fluorescence imaging [24], and near-infrared (NIR) spectroscopy [25] have been proposed to determine and predict FFB ripeness.…”

Section: Introductionmentioning

confidence: 99%

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Neural network with k-fold cross validation for oil palm fruit ripeness prediction

Shiddiq,

Candra,

Anand

et al. 2023

TELKOMNIKA

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The combination of hyperspectral imaging and artificial neural network (ANN) can predict fruit ripeness. This work investigated the application of hyperspectral imaging and ANN models with the k-fold cross-validation method for ripeness prediction of oil palm fresh fruit bunches (FFB) for inline sorting and grading machine vision. Crude palm oil (CPO) is an exporting commodity for countries such as Indonesia and Malaysia. Oil palm FFB ripeness determines the quality of CPO. The unique shapes and colors of FFBs need innovative methods to substitute tedious and cumbersome manual sorting and grading. The oil palm FFB samples used in this study were categorized previously based on color and loosed fruits. We applied the Savitzky-Golay (SG) smoothing filter and 7-fold cross-validation for hyperspectral datasets before being used for the ANN models and a confusion matrix to find the ANN model accuracies. We obtained 72 data points after SG filter and data selection from 523 data points. The prediction results showed an average accuracy of 79.48%, in which three folds with k of 2, 5, and 7 gave the highest accuracy of 90%. The results confirmed the potential use of hyperspectral imaging, with k-fold cross-validation and ANN models for ripeness prediction of oil palm FFBs.

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Section:  Issn: 1693-6930supporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Neural network with k-fold cross validation for oil palm fruit ripeness prediction

Shiddiq,

Candra,

Anand

et al. 2023

TELKOMNIKA

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“…Thus, careful consideration and proper measures should be taken to observe and understand the mechanics of the respiratory systems using the EIT devices. The further development of the EIT devices, both in hardware and software, could enhance the spatial resolution and scanning rates to higher numbers [37][38][39][40]. The developed 16 channel EIT system in this study could not only be useful for observation and monitoring the real-time lung imaging, but also in shading an insight into the understanding of circuitry importance and various aspects of clinical and experimental science.…”

Section: Discussionmentioning

confidence: 96%

Electrical Impedance Tomography Signal Conditioning for Lung Imaging Applications

Zulkiflli,

Nandaguru,

Arm

et al. 2023

HumEnTech

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Electrical impedance tomography (EIT) imaging is capable for human lung imaging based on its continuous self-monitoring capabilities, and suitability for daily routines. This paper introduces a simulation work for EIT signal conditioning circuit and its simulated waveform response using Multisim software. EIT circuit simulations consist of several signal processing circuits for the receiving part of the EIT, band pass filter circuit, amplifier, and analog-to-digital circuit. The system produced a unit function signal of 5V from an input 250 kHz sine function via band pass filter, operational amplifier and AC/DC conversion. The waveform pattern result is presented for each processing stage aim to demonstrate the basic work of an EIT circuitry setup.

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“…These include computer vision (using red-green-blue imaging) integrated with laser-light backscattering techniques 6 , spectroscopy 7 , hyperspectral imaging 8 , thermal imaging 9 , inductive frequency technique 10 and electrochemical sensor based on the fruit battery principle 11 . More recently, Raman 7,12,13 , Infrared 14 , NIR 15,16 , optical sensor 17 or diffuse reflectance spectroscopy 18 have also been used to classify oil palm fresh fruit maturity based on carotene and chlorophyll content. Carotene and chlorophyll are extracted along with palm oil during the extraction process from the palm fruit mesocarp.…”

mentioning

confidence: 99%

Enabling chlorophyll photo-response for in-line real-time noninvasive direct probing of the quality of palm-oil during mill process

Tan

Tiong²,

Adan³

et al. 2023

Sci Rep

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During the milling process of palm oil, the degree of palm fruit ripeness is a critical factor that affects the quality and quantity of the oil. As the palm fruit matures, its chlorophyll level decreases, and since chlorophyll in oil has undesirable effects on hydrogenation, bleachability, and oxidative degradation, it’s important to monitor the chlorophyll content in palm oil during the milling process. This study investigated the use of light-induced chlorophyll fluorescence (LICF) for non-invasive and real-time monitoring of chlorophyll content in diluted crude palm oil (DCO) located at the dilution and oil classification point in palm oil mill. An LICF probe was installed at the secondary pipe connected to main DCO pipeline, and the system communicates with a computer located in a separate control room via a Wi-Fi connection. Continuous measurements were recorded with an integration time of 500 ms, averaging of 10, and a time interval of 1 min between each recording during the oil mill’s operation. All data were stored on the computer and in the cloud. We collected 60 DCO samples and sent them to the laboratory for American Oil Chemists’ Society (AOCS) measurement to compare with the LICF signal. The LICF method achieved a correlation coefficient of 0.88 with the AOCS measurements, and it also provided a direct, quantitative, and unbiased assessment of the fruit ripeness in the mill. By incorporating Internet of Things (IoT) sensors and cloud storage, this LICF system enables remote and real-time access to data for chemometrics analysis.

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Non-Destructive Oil Palm Fresh Fruit Bunch (FFB) Grading Technique Using Optical Sensor

Cited by 6 publications

References 3 publications

Neural network with k-fold cross validation for oil palm fruit ripeness prediction

Neural network with k-fold cross validation for oil palm fruit ripeness prediction

Electrical Impedance Tomography Signal Conditioning for Lung Imaging Applications

Enabling chlorophyll photo-response for in-line real-time noninvasive direct probing of the quality of palm-oil during mill process

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