Camera-Vision Based Oil Content Prediction for Oil Palm (Elaeis Guineensis Jacq) Fresh Fruits Bunch at Various Recording Distances

Cherie, Dinah; Herodian, Sam; Mandang, Tineke; Ahmad, Usman

doi:10.18517/ijaseit.5.4.542

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…Nevertheless, these developed models only applicable to the tomato plants of this particular cultivar, others models should be developed for other tomato cultivar using similar principal, or by means of machine visions and spectroscopy [25][26][27][28][29][30][31][32][33][34][35][36][37][38].…”

Section: Resultsmentioning

confidence: 99%

Multi-modal Bio-metrics Evaluation for Non-destructive Age States Determination of Tomato Plants (Solanum lycopersicum)

Makky

2016

International Journal on Advanced Science, Engineering and Information Technology

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Every plant has unique morphological features, and can be used for its characteristics identity, such as age. When the plants grow, their morphological features may change, observable visually or by optical equipment. These various morphology transformations were categorized as multi-modal Bio-metrics. In this study, tomatoes from local cultivar were grown in a net house, in west Sumatra. The growth medium comprised of soil, husk, and manures with the composition of 1: 1: 1 respectively. For best growth, plants were watered regularly, and protect from pests and weeds. The observations were performed on 21st, 42nd, and 63rd day after sowing (DAS). The samples were the leaflets of the primary compound leaves of the plants. The leaflets were cut and digitized using a high-resolution colour scanner. The imaging performed at 300 dpi resolution, and the recorded image subsequently processed by the image processing software. Image segmentation performed to remove background from the object. Furthermore, the greenish of leaf object in the image were measured in RGB colour space. The leaf dimensions and area were quantified by the software, as well as the length of the leaflet main vein at central axis. Two secondary leaflet's blades were selected manually, and the angle formed between the blades and the main vein was measured. A Statistical engineering program was used to identify the principal morphology characteristics of the leaf, by means of Principal component analysis (PCA). Mathematical models were developed based on the principal component values and leaflets position to determine the plants age and state. Results showed all model have coefficient of correlation higher than 0.99 indicating acceptable accuracy.

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Section: Resultsmentioning

confidence: 99%

Multi-modal Bio-metrics Evaluation for Non-destructive Age States Determination of Tomato Plants (Solanum lycopersicum)

Makky

2016

International Journal on Advanced Science, Engineering and Information Technology

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“…Terdapat beberapa metode yang telah dikembangkan untuk menentukan tingkat kematangan TBS kelapa sawit secara obyektif. Salah satu metode yang paling banyak diusulkan adalah dengan menggunakan kamera (Kassim, 2014), (Cherie, 2015), (SUTAN, 2016). Dengan menggunakan metode ini, tingkat kematangan buah dapat dideteksi melalui warna TBS kelapa sawit, karena terdapat perbedaan warna antara TBS mentah, matang, dan busuk yang terjadi sebagai akibat dari proses kimia yang terjadi di dalam buah kelapa sawit.…”

Section: Pendahuluanunclassified

Pengembangan Sistem Penilaian Kematangan Tandan Buah Segar Kelapa Sawit menggunakan Citra 680 dan 750 NM

Setiawan¹,

Ananda²

2020

JTIIK

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Salah satu permasalahan utama dalam industri kelapa sawit adalah proses sortasi Tandan Buah Segar (TBS) di pabrik kelapa sawit. Parameter yang digunakan dalam sortasi TBS adalah jumlah brondolan kelapa sawit. Pada saat ini, sortasi dilakukan oleh grader yang bersifat subyektif dan sering kali tidak konsisten. Hal ini terjadi karena keterbatasan penglihatan dan kemampuan manusia untuk mengolah informasi jumlah brondolan setiap TBS dalam waktu yang terbatas. Oleh karena itu, pada penelitian ini dikembangkan sistem penilaian kematangan TBS kelapa sawit berbasis spektroskopi dan nilai kontras citras. Sumber cahaya yang digunakan pada penelitian ini adalah lampu berjenis Light-emitting Diode (LED) dengan panjang gelombang 680 dan 750 nm. Akuisisi citra TBS dilakukan dengan menggunakan kamera DSLR yang telah dimodifikasi. sehingga diperoleh dua citra TBS pada panjang gelombang 680 dan 750 nm. Kemudian, dilakukan perhitungan nilai kontras kedua citra tersebut. Dalam penelitian ini, terdapat 24 TBS yang digunakan sebagai data latih, dengan komposisi 10 TBS matang dan 14 TBS mentah. Data uji yang digunakan berjumlah 77 TBS yang terdiri dari 38 matang dan 39 mentah. Pada penelitian ini, Support Vector Machine (SVM) digunakan sebagai metode klasifikasi. Akurasi data latih yang diperoleh adalah 66,67%. Sedangkan akurasi data uji dari sistem yang dikembangkan dalam penelitian ini adalah 57,14%. Hasil yang diperoleh ini masih perlu diperbaiki untuk meningkatkan akurasi sistem dengan cara menambah jumlah data, baik data latih maupun uji, serta menggunakan pembelajaran mesin. AbstractOne of the main problems in the palm oil industry is the grading of Fresh Fruit Bunches (FFB) in the palm oil mills. The parameter used for the process is the number of fruitlets detached from the bunch. Nowadays, the FFB grading is conducted by graders which is subjective and often inconsistent due to the limitation of human vision and ability to process information on the number of fruitlets detached per FFB in a very limited time. Therefore, this study developed a grading system to assess and estimate the FFB maturity based on spectroscopy and image contrast value. From the literature review, visible light and NIR spectrum in 680 and 780 nm can be used as light sources to detect the maturity level of FFB. DSLR camera is used to acquire the FFB image. Using this scheme, two FFB images in 680 and 750 nm are obtained. The next process is to calculate the image contrast. In this research, there are 24 FFB that are used as training data that consists of 10 ripe and 14 unripe. A total of 77 FFB are used as test data that consists of 38 ripe and 39 unripe. Support Vector Machine (SVM) is used in this research to classify the maturity level of FFB. The accuracy of the training dataset is 66.67%. Meanwhile, the accuracy of the test data is 57.14%. Future works will focus on enhancing accuracy of the system through increasing the number of training and testing data using machine learning.

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“…The ripeness and maturity information are needed in order to harvest the good quality and ripe FFB. Lamps with certain wavelength have been used to assess the optical characteristics of FFB with three spectrum regions influence for Harvest Decision [15].…”

Section: Introductionmentioning

confidence: 99%

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

Shiddiq¹,

Fitmawati²,

Anjasmara³

et al. 2017

AIP Conference Proceedings

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Camera-Vision Based Oil Content Prediction for Oil Palm (Elaeis Guineensis Jacq) Fresh Fruits Bunch at Various Recording Distances

Cited by 10 publications

References 7 publications

Multi-modal Bio-metrics Evaluation for Non-destructive Age States Determination of Tomato Plants (Solanum lycopersicum)

Multi-modal Bio-metrics Evaluation for Non-destructive Age States Determination of Tomato Plants (Solanum lycopersicum)

Pengembangan Sistem Penilaian Kematangan Tandan Buah Segar Kelapa Sawit menggunakan Citra 680 dan 750 NM

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

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