scholarly journals Potential Content of Palm Oil at Various Levels of Loose Fruit in Oil Palm Circle

2021 ◽  
pp. 91-98
Author(s):  
Fitrah Murgianto ◽  
Edyson Edyson ◽  
Adhy Ardiyanto ◽  
Shadiar Kesuma Putra ◽  
Lilik Prabowo
Keyword(s):  
Palm Oil ◽  
Oil Palm ◽  
Oil Content ◽  
Fresh Fruit ◽  
Ripe Fruit ◽  
Palm Fruit ◽  
Palm Trees ◽  
Fresh Fruit Bunches ◽  
Potential Content ◽  
Important Activity

Harvesting fresh fruit bunches (FFB) is an important activity in the oil palm plantation industry. This study aimed to analyze the potential content of palm oil at the level of loose fruit that falls on the oil palm circle. Observations were made on five fresh fruit bunches with criteria 1, 3, and 5 respectively loose fruit per bunch that falls on the oil palm circle from oil palm trees that were 22, 16, 12, and 7 years old. All sample fresh fruit bunches were analyzed for potential oil to bunch and oil to wet mesocarp in the analytical laboratory of Bumitama Gunajaya Agro. Content of oil to wet mesocarp in loose fruit 1, 3, and 5 were 48,50 % b, 51,98 % a, and 53,21 % a respectively. While the content of oil to bunch in loose fruit 1, 3, and 5 were 24,19 % a, 25,52 % a, and 25,71 % a respectively. The highest potential for oil content occurs in ripe palm fruit with a level of loose fruit 5 grains per bunch that falls on that oil palm circle. Five grains per bunch on the oil palm circle can be used as an indicator for harvesters to harvest ripe fruit with optimal oil content.   

scholarly journals STUDY OF THE UTILIZATION OF PALM OIL INDUSTRY LIQUID WASTE

Konversi ◽  
2021 ◽  
Vol 10 (1) ◽  
Author(s):  
An Nisa Fitria ◽  
Vandhie Satyawira Gunawan ◽  
Mardiah Mardiah
Keyword(s):  
Palm Oil ◽  
Oil Palm ◽  
Economic Value ◽  
Liquid Waste ◽  
Oil Industry ◽  
Fresh Fruit ◽  
Added Value ◽  
Palm Fruit ◽  
Fresh Fruit Bunches ◽  
Plantation Crops

Palm oil is one of the plantation crops that have high economic value and is growing rapidly. The wider the area of oil palm plantations in Indonesia, the more palm oil mills will process palm fresh fruit marks and produce waste from processed palm oil, namely solid waste and liquid waste. Each tonne of fresh fruit bunches (FFB) processed at the plant will potentially leave waste of about 23% empty palm oil, 4% wet decanter solid, 6.5% shell, 13% fiber, and 50% liquid waste. This review will discuss the utilization of palm oil mill liquid waste (LCPKS) which is organic material that still contains many benefits such as nutrients, therefore the application of liquid waste is an effort to recycle some of the nutrients (recycling nutrients) which is followed by harvesting fresh fruit bunches (FFB) from oil palm so that it will reduce the cost of fertilization which is classified as very high for oil palm cultivation. During the processing of oil palm fruit into palm oil in the palm oil industry, the remaining process is obtained in the form of liquid waste. If done properly, the liquid waste of the palm oil industry is considerable potential and can increase the added value of waste itself.Keywords: liquid waste industry, palm oil, utilization  

scholarly journals IMPLEMENTASI JARINGAN SYARAF TIRUAN (JST) DAN PENGOLAHAN CITRA UNTUK KlASIFIKASI KEMATANGAN TBS KELAPA SAWIT

2018 ◽  
Vol 15 (1) ◽  
pp. 36
Author(s):  
Minarni Minarni ◽  
Roni Salumbae ◽  
Zilhan Hasbi
Keyword(s):  
Oil Palm ◽  
Color Space ◽  
Human Perception ◽  
Image Data ◽  
Cost Effective ◽  
Fresh Fruit ◽  
Human Vision ◽  
Palm Fruit ◽  
Color Parameters ◽  
Fresh Fruit Bunches

The clasification of ripeness stages of oil palm fresh fruit bunches (FFBs) can be done using color parameters. These parameters are often evaluated by human vision, whose degree of accuracy is subjective which can cause doubt in judgement. Automatic clasifications offreshfruit bunches (FFBs) based on color parameters can be done using computer vision. This method is known as a nondestructive, fast and cost effective method. In this research, a MATLAB computer program has been developed which consists of RGB and HSV GUI which is used to record, display, and process FFB image data. The backpropagation artificial neural network (ANN) program is also developed which is used to classify the oil palm fruit fresh bunches (FFBs). Samples are fresh fruit bunches (FFB) of oil palm varieties of Tenera which comprise of Topaz, Marihat, and Lonsum clones. Each clone composed of three levels of ripeness represented by five fractions. The measurements were started by capturing images of oil palm, extracting RGB and HSV values, calculating weight values from the image database to make anANN program, preparing grid programs for oil palm FFBs, and comparing grading levels of oil palm FFBs using program and by harvester. This program successfully classified oil palm (FFBs) into three categories of ripeness which are unripe (F0 and F1), ripe (F1 and F1) and over ripe (F4 and F5). The RGB and HSV programs successfully classified 79 out of 216 FFBs or 36.57% and 106 out of 216 TBS or 49.07%. Respectively the HSV program is better than RGB program because the representation of HSV color space are more understood by human perception hence can be used in calibration and color comparison.

scholarly journals Ripeness Level Classification of Oil Palm Fresh Fruit Bunch Using Laser Induced Fluorescence Imaging

2019 ◽  
Vol 8 (3) ◽  
pp. 84-89
Author(s):  
Hefniati Ishak ◽  
Minarni Shiddiq ◽  
Ramma Hayu Fitra ◽  
Nadia Zakyyah Yasmin
Keyword(s):  
Fluorescence Imaging ◽  
Palm Oil ◽  
Oil Palm ◽  
Laser Light ◽  
Laser Induced Fluorescence ◽  
Fresh Fruit ◽  
Human Vision ◽  
Imaging Method ◽  
Crude Palm Oil ◽  
Fresh Fruit Bunches

Tingkat Kematangan Tandan Buah Segar (TBS) kelapa Sawit merupakan faktor penentu kualitas crude palm oil (CPO) yang dihasilkan pabrik kelapa sawit. Metode penyortiran TBS setelah panen atau sebelum memasuki proses perebusan pada umumnya dilakukan secara manual mengandalkan penglihatan dan pengalaman. Metode ini rentan kesalahan dan bersifat subyektif. Metode pencitraan berkembang sangat cepat karena kemajuan dalam bidang komputer dan teknik pengolahan citra, khususnya untuk sistem sortasi dan grading. Penelitian ini mengunakan metode pencitraan fluoresensi yang diinduksi laser untuk mengakses dan mengklasifikasi tingkat kematangan TBS kelapa sawit. Hubungan antara tingkat keabuan dan tingkat kekerasan buah TBS dianalisa. Sampel terdiri dari 27 TBS kelapa sawit varietas Tenera. Tingkat kematangan dikategorikan oleh pemanen berpengalaman menjadi mentah, matang, dan lewat matang. Tiga bagian TBS yaitu pangkal, tengah, dan ujung disinari laser dioda 640 nm mengenai 5 buah pada tiap bagian. Kemudian citra direkam mengunakan kamera CMOS monokrom. Selanjutnya 15 buah tersebut diuji tingkat kekerasan mengunakan penetrometer. Klasifikasi tingkat kematangan dilakukan mengunakan K-mean clustering. Hasil penelitian memperlihatkan bahwa metode pencitraan fluoresensi yang diinduksi laser potensial digunakan dalam mengklasifikasi tingkat kematangan TBS. Tingkat kekerasan buah berkorelasi positif terhadap tingkat keabuan citra TBS. K-mean clustering memperlihatkan tiga kelompok tingkat kematangan yang terdiri dari 0, 1 dan 2. Ripeness levels of oil palm fresh fruit bunches (FFB) are the main factor to determine the quality of crude palm oil (CPO) produced by Oil Palm Mill. Sorting oil palm FFB after harvest or before entering the boiling process is generally done manually which relies on human vision and experience. Imaging methods has developed vastly due to advances in computer and image processing techniques. This study used a laser-induced fluorescence imaging to access and classify the ripeness levels of oil palm FFB of Tenera variety. The relationship between gray value and the level of firmness of FFB fruit was analyzed. The samples consisted of 27 oil palm FFB categorized  by experienced harvester as unripe, ripe, and overripe. Laser light was shone on equatorial part of each FFB such that 5 fruitlets were covered by laser light, then the image of the front part was acquire using a monochrome CMOS camera. The step was repeated for basil and apical parts in sequent. All 15 fruitlets were testing for the firmness level using a penetrometer. Ripeness level classification was done using K-mean clustering. The results showed that the laser-induced fluorescence imaging method are potential to be used to determine the ripeness levels of FFB. The fruit firmness is positively correlated with the gray value of the image of FFB. K-mean clustering shows three ripeness centroid of 0, 1 and 2 . Keyword: Fluorecence Imaging, Oil Palm, Fresh Fruit Bunches, Firmness, Laser Induced Fluorecence

scholarly journals A RIPENESS STUDY OF OIL PALM FRESH FRUIT AT THE BUNCH DIFFERENT POSITIONS

2020 ◽  
Vol 8 (1) ◽  
pp. 9-14
Author(s):  
Rismen Sinambela
Keyword(s):  
Water Content ◽  
Oil Palm ◽  
Oil Content ◽  
Elaeis Guineensis ◽  
Soxhlet Extraction ◽  
Fresh Fruit ◽  
Test Method ◽  
Central Position ◽  
Fresh Fruit Bunch ◽  
Palm Fruit

The paper aims to study the position of the optimum oil palm ripeness at the bunch different positions. This information is essential to complete a measurement procedure to detect oil palm fresh fruit bunch (FFB) maturity so that the detection devices can directly measure the optimal mature position as a representative of the entire FFB characteristics. In this study, the oil palm FFB (Elaeis guineensis Jacq. var. tenera) with the various ripeness stages (4 until 22 weeks after anthesis) were collected and divided from three positions, i.e., proximal, central and distal. Moreover, each fruit in each of these positions was subjected to sample preparation to identify water and oil content. The water and oil content were completed based on the oven test method and the Soxhlet extraction technique, respectively. The optimum ripeness position is determined based on the lowest water content and the highest oil content. Based on the analysis, during the process of oil palm maturation occurs a decrease in water content and an increase in oil content. In addition, the average water content of palm fruit varies greatly depending on its position based on the analysis, i.e., proximal (45.38±5.62%), central (35.30±3.34%) and distal (41.98±2.57%). The average oil content of oil palm fruit in the central position is higher oil content (25.10±1.72%) compared to the proximal (10.00±0.77%) and distal position (13.77±1.22%). We suspect that the chemical content differences of palm fruit in various positions are due to the inequality of the respiration rate and ethylene production throughout FFB. In addition, overall it can be concluded that the fruit in the central FFB position has an optimal ripeness level compared to the proximal and distal position. Thus, the measurement position recommended in evaluating palm maturity is at the central position of FFB.

scholarly journals Development of Image Based Modeling for Determination of Oil Content and Days Estimation for Harvesting of Fresh Fruit Bunches

2009 ◽  
Vol 5 (2) ◽  
Author(s):  
Mohd. Hudzari Razali ◽  
Wan Ishak Wan Ismail ◽  
Abdul Rahman Ramli ◽  
Md. Nasir Sulaiman ◽  
Mohd. Haniff Harun
Keyword(s):  
Oil Palm ◽  
Oil Content ◽  
Oil Extraction ◽  
Fresh Fruit ◽  
Extraction Rate ◽  
Outdoor Environment ◽  
Oil Palm Plantation ◽  
Fresh Fruit Bunches ◽  
Palm Fruits ◽  
Relationship Of

In this study, the relationship of oil extraction rate (OER) and fruit ripeness will be determined. The sample of oil palm fruits was collected from the unripe until the overripe stage and the oil content of the mesocarp for fresh fruit bunches (FFB) was extracted by using bunch analysis procedure to get the oil extraction rate. Using the same samples of FFB, the pixel value of images which measure in hue, was determined by developed image analysis software. The images were captured under an outdoor environment in an oil palm plantation. The sunlight intensity of environment was recorded using Extech light meter at various times of the day from morning to afternoon in the oil palm plantation. The result of the experiment that showed a good relationship was found between the oil content of FFB with its image pixel values. The mathematical model was developed in determining the optimum days for FFB harvesting.

scholarly journals Implementation of Backpropagation Artificial Neural Networks to Predict Palm Oil Price Fresh Fruit Bunches

2018 ◽  
Vol 2 (1) ◽  
pp. 26
Author(s):  
Edi Ismanto ◽  
Noverta Effendi ◽  
Eka Pandu Cynthia
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Palm Oil ◽  
Oil Palm ◽  
Influential Factors ◽  
Fresh Fruit ◽  
Selling Price ◽  
Architecture Model ◽  
Artificial Neural ◽  
Fresh Fruit Bunches

Riau Province is one of the regions known for its plantation products, especially in the oil palm sector, so that Riau Province and regional districts focus on oil palm plants as the main commodity of plantations in Riau. Based on data from the Central Bureau of Statistics (BPS) of Riau Province, the annual production of oil palm plantations, especially smallholder plantations in Riau province has always increased. So is the demand for world CPO. But sometimes the selling price of oil palm fresh fruit bunches (FFB) for smallholder plantations always changes due to many influential factors. With the Artificial Neural Network approach, the Backpropagation algorithm we conduct training and testing of the time series variables that affect the data, namely data on the area of oil palm plantations in Riau Province; Total palm oil production in Riau Province; Palm Oil Productivity in Riau Province; Palm Oil Exports in Riau Province and Average World CPO Prices. Then price predictions will be made in the future. Based on the results of the training and testing, the best Artificial Neural Network (ANN) architecture model was obtained with 9 input layers, 5 hidden layers and 1 output layer. The output of RMSE 0000699 error value and accuracy percentage is 99.97% so that it can make price predictions according to the given target value.

scholarly journals STUDI PERBANDINGAN RANTAI PEMASARAN TANDAN BUAH SEGAR KELAPA SAWIT DI DESA JONGGON KABUPATEN KUTAI KARTANEGARA

2020 ◽  
Vol 5 (02) ◽  
pp. 142-150
Author(s):  
Ahmad Reza Vahlevi ◽  
Ernita Obeth ◽  
Budi Winarni ◽  
Budi Winarni
Keyword(s):  
Supply Chain ◽  
Palm Oil ◽  
Oil Palm ◽  
Marketing Channel ◽  
Fresh Fruit ◽  
Raw Material ◽  
Crude Palm Oil ◽  
Oil Processing ◽  
Cooperative Processing ◽  
Fresh Fruit Bunches

This research is motivated by oil palm farmers, which is in contrast to the high demand for fresh fruit bunches, as the main raw material for producing crude palm oil. On the other hand, oil palm smallholders are also involved in several different supply chains. The purpose of this study was to determine the management of fresh fruit bunches  marketing and the amount of profit received by farmers through the marketing of oil palm fresh fruit bunches  in Jonggon Village, Kutai Kartanegara Regency. The analytical method used is descriptive quantitative and marketing margin analysis. The method of determining respondents used purposive sampling method and the respondents in this study were plasma and non-plasma farmers, collector traders and plasma cooperatives, and crude palm oil processing factories owned by PT. Niaga Mas Gemilang in Jonggon Village, Kutai Kartanegara Regency. The results of the respondents' research are in 2 running marketing channels, namely the first marketing channel, namely farmer-cooperative-processing factory, and the second one is farmer-trader-processing factory. Farmers involved in the first supply chain get a profit of Rp. 900 / kg and the farmers involved in the second supply chain get a profit of Rp. 1070 / kg.

scholarly journals Effect of Charging Parameter on Fruit Battery-Based Oil Palm Maturity Sensor

Micromachines ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 806
Author(s):  
Norhisam Misron ◽  
Nisa Syakirah Kamal Azhar ◽  
Mohd Nizar Hamidon ◽  
Ishak Aris ◽  
Kunihisa Tashiro ◽  
...  
Keyword(s):  
Palm Oil ◽  
Oil Palm ◽  
Load Resistance ◽  
Oil Extraction ◽  
High Demand ◽  
Palm Fruit ◽  
Charging Time ◽  
Fresh Fruit Bunches ◽  
Content Processing ◽  
Key Industries

Oil palm is one of the key industries highly observed in Malaysia, due to its high demand both whether locally or internationally. The oil extraction rate (OER) in palm oil production is used as an element to identify the performance of the mills, estates and producers. In view of this, there are specific instrument or sensor needs to be implemented at the mills especially during the reception of fresh fruit bunches (FFB) transported from the field for oil content processing. This paper aims to study and propose the use of a fruit battery-based oil palm maturity sensor to analyse the effect of the sensor to various parameters. The study utilizes a charging method with different parameters, including a moisture content test on the palm oil samples. Three types of parameters are tested along with the different grades of oil palm fruit from different bunches, such as the load resistance, charging voltage and charging time. The repeatability data of the samples are obtained with the used list of values in each parameter. The results show that the parameters tested for the unripe, under ripe and ripe samples can affect the sensor sensitivity.

scholarly journals Classification of Oil Palm Fresh Fruit Bunches (FFB) Using Raman Spectroscopy

2018 ◽  
Vol 7 (4.11) ◽  
pp. 184
Author(s):  
S. A.M. Dan ◽  
F. H. Hashim ◽  
T. Raj ◽  
A. B. Huddin ◽  
A. Hussain
Keyword(s):  
Raman Spectroscopy ◽  
Oil Palm ◽  
Fresh Fruit ◽  
Carotenoid Content ◽  
Raman Shift ◽  
Palm Fruit ◽  
Fresh Fruit Bunches ◽  
Different Characteristics ◽  
Extraction Model

The current practice in determining oil palm fresh fruit bunches (FFB) ripeness is by its colour which could be inaccurate. This study investigates the classification of oil palm FFB ripeness using Raman spectroscopy. A feature extraction model is developed based on the different organic compositions that contribute to the ripeness classification. Samples are collected according to the Malaysian Palm Oil Board (MPOB) standards which are unripe, underripe, ripe, overripe, and rotten. Different characteristics of the Raman shift were detected which represent the material composition for each sample. The Raman intensity of the oil palm fruit increases from unripe to ripe before decreasing to rotten due to the carotenoid content in the fruit. In conclusion, Raman spectroscopy is a suitable technique to observe the changes in the composition of oil palm fruit classified by its ripeness.  

scholarly journals TREND IN THE DEVELOPMENT OF OIL PALM FRUIT HARVESTING TECHNOLOGIES IN MALAYSIA

2018 ◽  
Vol 80 (2) ◽  
Author(s):  
Sharence Nai Sowat ◽  
Wan Ishak Wan Ismail ◽  
Muhammad Razif Mahadi ◽  
Siti Khairunniza Bejo ◽  
Muhamad Saufi Mohd Kassim
Keyword(s):  
Oil Palm ◽  
Physical Characteristics ◽  
Design Criteria ◽  
Manual Labor ◽  
Labor Shortage ◽  
Climbing Robots ◽  
Tall Oil ◽  
Palm Fruit ◽  
Palm Trees ◽  
Fresh Fruit Bunches

Harvesting oil palm fresh fruit bunches (FFB) on tall oil palm trees is a laborious and hazardous task. Lately, with the escalating problem of labor shortage, the exigent demand to mechanize the harvesting task cannot be overlooked. Over the years, many harvesting methods and technologies have been used and developed to increase the harvesting productivity. This paper reviews the conventional manual harvesting using manual labor, mechanization of harvesting task using harvesting machines as well as research on climbing robots for harvesting FFB in Malaysia. In essence, it provides an overview of the trend in the development of harvesting technologies in Malaysia. Realizing the potential of climbing robots for harvesting, the morphological structures and physical characteristics of oil palm trunks in its natural surroundings are examined closely to identify the challenges in the climbing and harvesting processes. Next, a set of design criteria is introduced to overcome those challenges. In addition, several mechanisms are proposed which play integral parts in enhancing the climbing and harvesting tasks. 

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