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 Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

Agriculture ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 97 ◽  
Author(s):  
Kelvin López-Aguilar ◽  
Adalberto Benavides-Mendoza ◽  
Susana González-Morales ◽  
Antonio Juárez-Maldonado ◽  
Pamela Chiñas-Sánchez ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dry Matter ◽  
Fruit Yield ◽  
Fresh Fruit ◽  
Neural Network Modeling ◽  
Peat Moss ◽  
Tomato Crop ◽  
Marquardt Algorithm ◽  
Artificial Neural

Non-linear systems, such as biological systems, can be simulated by artificial neural network (ANN) techniques. This research aims to use ANN to simulate the accumulated aerial dry matter (leaf, stem, and fruit) and fresh fruit yield of a tomato crop. Two feed-forward backpropagation ANNs, with three hidden layers, were trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted. The input layer consisted of the leaf area, plant height, fruit number, dry matter of leaves, stems and fruits, and the growth degree-days at 136 days after transplanting (DAT); these were obtained from a tomato crop, a hybrid, EL CID F1, with indeterminate growth habits, grown with a mixture of peat moss and perlite 1:1 (v/v) (substrate) and calcareous soil (soil). Based on the experimentation of the ANNs with one, two and three hidden layers, with MSE values less than 1.55, 0.94 and 0.49, respectively, the ANN with three hidden layers was chosen. The 7-10-7-5-2 and 7-10-8-5-2 topologies showed the best performance for the substrate (R = 0.97, MSE = 0.107, error = 12.06%) and soil (R = 0.94, MSE = 0.049, error = 13.65%), respectively. These topologies correctly simulated the aerial dry matter and the fresh fruit yield of the studied tomato crop.

scholarly journals Quality Prediction Modeling of Palm Oil Refining Plant in Malaysia Using Artificial Neural Network Models

2018 ◽  
Vol 7 (3.26) ◽  
pp. 19
Author(s):  
Nurul Sulaiha Sulaiman ◽  
Khairiyah Mohd-Yusof ◽  
Asngari Mohd-Saion
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Palm Oil ◽  
Oil Refinery ◽  
Oil Refining ◽  
Great Loss ◽  
Refining Plant ◽  
Artificial Neural ◽  
Refined Palm Oil

Malaysia is currently one of the biggest producers and exporters of palm oil and palm oil products. In the growth of palm oil industry in Malaysia, quality of the refined oil is a major concern where off-specification products will be rejected thus causing a great loss in profit. In this paper, predictive modeling of refined palm oil quality in one palm oil refining plant in Malaysia is proposed for online quality monitoring purposes. The color of the crude oil, Free Fatty acid (FFA) content, bleaching earth dosage, citric acid dosage, activated carbon dosage, deodorizer pressure and deodorizer temperature were studied in this paper. The industrial palm oil refinery data were used as input and output to the Artificial Neural Network (ANN) model. Various trials were examined for training all three ANN models using number of nodes in the hidden layer varying from 10 to 25. All three models were trained and tested reasonably well to predict FFA content, red and yellow color quality of the refined palm oil efficiently with small error. Therefore, the models can be further implemented in palm oil refinery plant as online prediction system.  

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 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  

Use of Binary Sigmoid Function And Linear Identity In Artificial Neural Networks For Forecasting Population Density

2017 ◽  
Vol 1 (1) ◽  
pp. 43 ◽  
Author(s):  
Anjar Wanto ◽  
Agus Perdana Windarto ◽  
Dedy Hartama ◽  
Iin Parlina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Population Density ◽  
Network Architecture ◽  
Activation Function ◽  
Sigmoid Function ◽  
Backpropagation Algorithm ◽  
Architecture Model ◽  
Artificial Neural

Artificial Neural Network (ANN) is often used to solve forecasting cases. As in this study. The artificial neural network used is with backpropagation algorithm. The study focused on cases concerning overcrowding forecasting based District in Simalungun in Indonesia in 2010-2015. The data source comes from the Central Bureau of Statistics of Simalungun Regency. The population density forecasting its future will be processed using backpropagation algorithm focused on binary sigmoid function (logsig) and a linear function of identity (purelin) with 5 network architecture model used the 3-5-1, 3-10-1, 3-5 -10-1, 3-5-15-1 and 3-10-15-1. Results from 5 to architectural models using Neural Networks Backpropagation with binary sigmoid function and identity functions vary greatly, but the best is 3-5-1 models with an accuracy of 94%, MSE, and the epoch 0.0025448 6843 iterations. Thus, the use of binary sigmoid activation function (logsig) and the identity function (purelin) on Backpropagation Neural Networks for forecasting the population density is very good, as evidenced by the high accuracy results achieved.

Prediction of Mine Inrush Water Based on BP Neural Network Method

2014 ◽  
Vol 989-994 ◽  
pp. 1814-1820 ◽  
Author(s):  
Ai Jun Shao ◽  
Qing Xin Meng ◽  
Shi Wen Wang ◽  
Ying Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Mine Water ◽  
Influential Factors ◽  
Water Inflow ◽  
Impact Factors ◽  
Artificial Neural ◽  
Inrush Water ◽  
The Impact

Based on predictions of the mine inflow of water and the complexity of influential factors, a method of BP neural network is put forward for mine inrush water prediction in this paper. We chose proper impact factors and establish non-linear artificial neural network prediction model after analyzed the impact factors of mine water inflow in Shandong Heiwang iron, and also made one prediction with normal mine water inflow during the iron mining operation. It turned out that the result can match with the actual prediction data, which make it possible to predict the mine water inflow with the prediction of Artificial Neural Network.

Sign in / Sign up

Export Citation Format

Share Document