Classification of oil palm fresh fruit bunches based on their maturity using portable four-band sensor system

Saeed, Osama Mohamed Ben; Sankaran, Sindhuja; Shariff, Abdul Rashid Mohamed; Shafri, Helmi Zulhaidi Mohd; Ehsani, Reza; Alfatni, Meftah Salem M.; Hazir, Mohd Hafiz Mohd

doi:10.1016/j.compag.2011.12.010

Cited by 75 publications

(41 citation statements)

References 19 publications

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“…Analisa dilakukan berdasarkan spektrum yang diperoleh dari Brix, pH, dan konsentrasi anthocyanin. Ben Saeed dkk [4], menggunakan Optical Sensor System Comprising pada frekuensi 570, 670, 750, dan 870 nm untuk mendeteksi tingkat kematangan buah kurma sedangkan klasifikasinya menggunakan metode quadratic discriminant classifier dengan hasil akurasi sebesar 85%.…”

Section: Pendahuluanunclassified

Color Grading Systems to Classify Ripeness of Apple Mango Fruit

Prayogi¹,

Budiman²

2018

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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Harvesting of apple mangoes fruit that simultaneously causes not all apple mango fruits are harvested ripe. Farmers should sort the apple mango manually. So, harvesting in large quantities will take a long time. There are several obstacles to sort the apple mango fruit manually. It needs skills and experiences to sort the apple mangoes fruit and it needs many workers. In addition, when the apple mango is ripe and not immediately sorted, it will be too ripe and decayed, so cause losses. Previous research has already made the apple mango classification system mango level but required an expensive tool and take a long time to process one apple mango. This research proposes a new strategy to measure the ripeness level of apple mango fruit, especially apple mango at low cost, without the expensive device and takes a short time based on the apple mango skin color region using the feed-forward neural network. The test used 214 images consisting of 4 classes. Validation test using k-fold of 7, 9, and 11 with an average accuracy of 90.61%, 91.41%, and 90.82%. Highest accuracy with a value of 91.41% on k-fold 9 while at k-fold 11 accuracy is lower but has the least standard deviation value. A small standard deviation means that the accuracy is more stable than others.

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Section: Pendahuluanunclassified

Color Grading Systems to Classify Ripeness of Apple Mango Fruit

Prayogi¹,

Budiman²

2018

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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“…Once the optical properties obtained on the surface of the palm tree fruits are used as an indicator of maturation, and the oil contents contained in the mesocarp of the fruits are directly associated to the point of physiological maturation. The different spectral responses can be used to estimate the amount of oil (Saeed et al, 2012, Matsimbe et al, 2015, Costa et al, 2017.…”

Section: Introductionmentioning

confidence: 99%

Classification of Macaw Palm Fruits From Colorimetric Properties for Determining the Harvest Moment

et al. 2018

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Macaw palm (Acrocomia aculeata) is a promising crop for biofuel production due to the high concentration of its fruit oil, but the harvest date is an issue to be better understood so it could be cultivated on an industrial scale. The aim of this study was to use the colorimetric properties of the macaw palm fruits to develop a neural network classifier to determine the ideal moment for harvesting, based on the oil content of the fruit mesocarp. During nine weeks of maturation were sampled 900 fruits of macaw palm fruits and the colorimetric properties of the RGB, HSI and CIELab color models were used to classify the fruits into immature and mature fruits. Kappa index and the overall accuracy values were used to access the classifier performance. The classifiers based on RGB parameters and on hue were considered equivalents having a Kappa index of 0.901 and 0.942, respectively, indicating the 59 th week of maturation as the ideal time to harvest with the highest oil content.

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“…Optical instruments are being applied as an alternate to classify and control fruit and vegetable quality, with the use of spectral reflection data (Saeed et al, 2012;Viegas et al, 2016), the use of fluorescent sensors to determine maturity , the use of digital images (Tan et al, 2010) and the use of biospeckle laser for quality and monitoring of biologic activity Zdunek and Herppich, 2012).…”

Section: Introductionmentioning

confidence: 99%

Determination of macaw fruit harvest period by biospeckle laser technique

Costa¹,

Pinto²,

JÃonior³

et al. 2017

Afr. J. Agric. Res.

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Macaw palm has been stood out as a raw material for the production of bioenergy, because it has high productivity of oil and less emission of polluting waste during combustion, meeting the worldwide demand for sustainable energy sources. The aims of this research were the evaluation of response of the biologic activity measured by the optical technique of the biospeckle laser applied to macaw palm fruits at different maturity weeks and develop a classifier in function of biologic activity to determine the harvest period related with oil content in the fruits. To perform the experiment, 10 weeks fruits different maturity stages were evaluated. The biospeckle laser images were obtained by illuminating the epicarp of each fruit. The biological activity was quantified by absolute value of difference algorithm applied to biospeckle images. A neural network was developed to classify the fruits which were closer to harvest in function of biologic activity. Biologic activity showed a significant linear ratio (R 2 = 0.913) with the maturation of fruits. Classification results have shown that fruits from 59 th week after flowering are ideal for harvest and present the highest oil levels.

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Classification of oil palm fresh fruit bunches based on their maturity using portable four-band sensor system

Cited by 75 publications

References 19 publications

Color Grading Systems to Classify Ripeness of Apple Mango Fruit

Color Grading Systems to Classify Ripeness of Apple Mango Fruit

Classification of Macaw Palm Fruits From Colorimetric Properties for Determining the Harvest Moment

Determination of macaw fruit harvest period by biospeckle laser technique

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