Prediction of Output Energy based on Different ...

Agric. sci. dev., Vol(5), No (2), June, 2016. pp. 14-21

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Agriculture Science Developments

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Prediction of Output Energy based on Different Energy Inputs on Broiler Production using Application of Adaptive Neural-Fuzzy Inference System Sama Amid * Department of Agricultural Machinery Engineering, Faculty of Agriculture Technology and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.

Tarahom Mesri Gundoshmian Department of Agricultural Machinery Engineering, Faculty of Agriculture Technology and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran. *Corresponding author: [email protected]

Keywords

Abstract

Adaptive neuro-fuzzy inference systems Artificial neural networks Broiler farms Energy consumption Prediction

An Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Artificial Neural Networks (ANNs) is used for modeling energy output on the basis of energy inputs in broiler production. Data of study were randomly collected from 70 broiler farms in Mountainous region in North West of Iran. Energy inputs used in broiler production included labor, machinery, fuel, feed and electricity in which were all selected as input parameters of the models and correspondingly energy output produced was considered as output variable. The best ANN model had 5-12-1 structure, i.e., it consisted of an input layer with five input variables, a hidden layer with 12 neurons and broiler energy as output. Also, the best ANFIS model was developed and generalized to compare their results with proposed ANN model. Correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE) were computed as 0.81, 0.05 and 0.31 for the best ANN model and they were calculated as 0.995, 0.007 and 0.046 for ANFIS model. Based on the obtained results of this study, it can be conclude that ANFIS model gives better results than ANN model to predict output energy based on inputs energy consumption.

1.

Introduction

Poultry meat and eggs offer considerable potential of human needs for carnivorous dietary. FAO recommendation for daily protein consumption is put at 60 grams per person out of which 35 grams is expected to be of animal source [7]. Poultry are acting efficiently in conversion of feed to egg and meat within a short period of time [35]. Broiler is an important source of high quality proteins, minerals and vitamins to balance the human diet. The third position of consumed meat in the world is allocated to broiler consumption after beef and pork [2]. Nowadays, agricultural sector has become more energy-intensive in order to supply more food to increasing population and provide sufficient and adequate nutrition. However, considering limited natural resources and the impact of using different energy sources on environment and human health, it is substantial to investigate energy use patterns in agriculture [9]. The relationship between agriculture and energy is meaningful due to the fact that agriculture itself is an energy user and energy supplier in the form of bio-energy. Energy consumption in agriculture largely depends on technologies, which are employed in farming systems, in addition to the level of production [32]. Effective energy use in agriculture is one of the key ways to reduce the energy consumption, since it provides financial savings, fossil resources preservation and air pollution reduction [28]. In the past, mathematical models were used to find the relationships between inputs and outputs of a production process. But this classic logic approach requires an exact definition of the mathematical model equations to describe the phenomenon. Today, it is known that the fuzzy logic, an Artificial Intelligence (AI) method, offers the mathematical framework. Because the fuzzy logic allows for a simple knowledge representation of the production process in terms of IF-THEN rules [24]. Artificial neural networks (ANNs), another AI approach, are one of the most efficient computational methods rather than other analytical and statistical techniques. Since agricultural systems and technologies are quite complicated and uncertain, they can be widely applied for modeling of different components in this sector [15]. Artificial neural networks (ANNs) have been widely applied to predict energy consumption, energy demand, environmental problems and etc [16]. In recent years, several energy studies have been conducted using ANNs [6, 31, 39, 33, 27, 23]. Comparison between ANN and an MLR model for predicting the milk yield in dairy cows in Canada showed that the results of ANN models were relatively better than those of MLR models [8]. In another study, Heidari et al. developed various Artificial Neural Network models do estimate the Benefit to Cost Ratio (BCR) of broiler farms in tropical regions of Iran [11]. Application of ANNs to estimate egg production farms in Iran was reported by Sefeedpari et al. [36]. They used an ANN model with three input variables including fuel energy (including fossil based fuels), electricity, and feed energies to predict energy consumption of poultry for egg production. Adaptive neuro-fuzzy inference system (ANFIS), as another AI method and is a beneficial method to solve non-linear problems. ANFIS is a combination of ANN and fuzzy systems and provides the benefits of two models [24]. Reviewing the literature reveals that some agricultural studies have been conducted using ANFIS models [29, 24, 19]. Recently, Khoshnevisan et al. applied adaptive neural-fuzzy inference system (ANFIS) to predict the potato yield on the basis of different energy inputs in Iran [14]. In their study, a multi layered ANFIS approach showed that the applying ANFIS with multiple layers could predict the grain yield with good accuracy better than does ANN model. Also, Khoshnevisan et al. analyzed greenhouse strawberry yield using a neuro-fuzzy approach [15]. They came to the conclusion that adaptive neural-fuzzy inference system (ANFIS) is effective in comparison to neural networks. In another study, Sefeedpari et al. showed the application of ANFIS for prediction of milk yield in dairy farms based on energies of fossil fuels and electricity inputs [38]. Accordingly the main objective of this study is to develop an ANFIS model to predict broiler production on the basis of input energies and to compare the obtained results with a developed ANN model.

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Prediction of Output Energy based on Different Energy Inputs on Broiler Production using Application of Adaptive Neural-Fuzzy Inference System Agriculture Science Developments Vol(5), No (2), June, 2016.

2.

Methodology

2.1 Data preparation Data for this study were obtained from 70 broiler farmers using face-to-face questionnaires in Ardabil province of Iran. The province is located in the northwest of Iran, within 47◦ 15‫ ׳‬and 48◦ 56‫ ׳‬east longitude and 37◦ 09‫ ׳‬and 39◦ 42‫ ׳‬north latitude [21]. Data were collected in SeptemberOctober 2013 period. The sample size was determined, using the Cochran technique [5]. Accordingly 70 broiler farmers were randomly selected to obtain relevant information. Input energy sources included human labor, machinery, fuel, feed and electricity; while output energy source was broiler. The energy equivalent to each input and output in crop production has been presented in Table 1. Energy equivalents were determined by multiplying the quantity per 1000 birds by their conversion factors. Table 1. Energy coefficients of inputs and outputs in broiler production

Items A. Inputs 1. Human labor 2. Machinery Polyethylene Galvanized iron Steel Electric motor 3. Fuel Diesel 4. Feed Maize Soybean meal Di-calcium phosphate Minerals and vitamins Fatty acid 5. Electricity B. Outputs 1. Broiler

Units

Energy equivalent (MJ unit-1)

Reference

h

1.96

[22]

kg kg kg kg L

46.3 38 62.7 64.8 47.8

[18] [37] [4] [4] [17]

kg kg kg kg kg kWh

7.9 12.06 10 1.59 9 11.93

[2] [2] [1] [34] [10] [26]

kg

10.33

[10]

2.2 Artificial neural networks ANNs are simplified mathematical models of biological operations of the brain [36]. ANN has been widely used for simulating system performance especially when the simulation of complex systems is required but limited experimental data is available [25]. Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous surge in research activities in the past two decades. They can also be configured in various arrangements to perform a range of tasks including classification, pattern recognition, data mining and process modeling [40, 27]. A widely used ANN architecture is called the multi-layer perceptron (MLP). The MLP consists of one input layer, one or more hidden layers and one output layer. Each layer includes a number of elements called neurons or nodes. The input and output layers are connected by a hidden layer. An ANN structure usually consists of a layer of input neurons, a layer of output neurons and one or more hidden layers [23]. All links between input layers and hidden layers composed the input weight matrix and all links between hidden layers and output layers composed the output weight matrix. Weight (w) which controls the propagation value (x) and the output value (O) from each node is modified using the value from the preceding layer according to [41]:

O  f T  w

i

x  i

Where T is a specific threshold (bias) value for each node. f is a non-linear sigmoid function, which increased monotonically. In order to predict the energy consumption of broilers, MATLAB R2013a software was used. So, 70 units of broiler farms in the province of Ardabil, was entered in the model. Factors of labor, machinery, fuel, feed, and electricity energy were inputs, and broiler energy content was used as the expected values. Before starting simulation for design of artificial neural network, data are divided into two categories, training data and test data. ANN was trained with 70 percent of study data, and at the lowest MSE, weights were found their final values on a way that network was giving the lowest error for the training data. The remaining data (30%) were also identified to the test model that played no role in the training data and given as input value to the network and its response is compared with the desired response of the model and the efficiency of the trained network is verified. Several structures were evaluated using the experimental data to determine the best predicting model by the network. The number of neurons was determined for input and output layer based on number of inputs and output for broiler production. Also, one and two hidden layers were applied for ANN modeling and according to the best results, one of them was proposed for modeling. Levenberg–Marquardt learning Algorithm was used for training ANNs that are the widely used algorithms, which very fast on training network algorithm with minimum existing error. 2.3 Adaptive neuro-fuzzy inference system ANFIS is a neuro-fuzzy system developed by Jang [13]. It has a feed-forward neural network structure where each layer was a neuro-fuzzy system component. The ANFIS model based on fuzzy Sugeno model put in the framework of adaptive systems to facilitate learning and adaptation. Such framework makes the ANFIS modeling more systematic and less reliant on expert knowledge. To present the ANFIS architecture, two fuzzy if–then rules based on a first order Sugeno model are considered [30, 12]:

Rule 1: If (x is A1) and (y is B1 ) then (f1=p1x+q1y+r1) Rule 2: If (x is A2) and (y is B2 ) then (f2=p2x+q2y+r2) where x and y are the inputs, Ai and Bi are the fuzzy sets, fi are the outputs within the fuzzy region specified by the fuzzy rule, pi, qi and ri are the design parameters that are determined during the training process. The ANFIS architecture to implement these two rules is shown in Figure 1,

Sama Amid *, Tarahom Mesri Gundoshmian

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Agriculture Science Developments Vol(5), No (2), June, 2016.

includes 5 layers. The first layer contains membership functions (MFs). The most common MF encompasses triangular and bell-shaped. The second layer computes the firing robustness of a rule multiplication. The third layer presents outputs called normalized firing strengths. The output of the layer 4 is compressed of a linear combination of the inputs multiplied by the normalized firing strength w. The layer 5 is the simple summation of the outputs of layer 4 [30, 3].

Figure 1. The architecture of ANFIS network

To obtain the best results several modifications were made in the structure of ANFIS networks, and some parameters were calculated to compare the results of different models. The number of membership functions, types of membership functions (triangular, trapezoidal, bell-shaped, gaussian and sigmoid), and types of output membership functions (constant or linear), optimized methods (hybrid or back propagation) and number of epochs were adjusted to gain the best ANFIS model [15]. In order to create FIS using ANFIS, fuzzy logic toolbox of MATLAB (R2013a) was used. Input signals in the modeling formulation in this study, was the energy used in the production of broilers. The Number of energy Inputs or in other words the number of input variables for modeling using neuro-fuzzy inference system for broiler production is five features, But because of the low number of studied units, it has been tried to reduce the number of inputs as possible. So some of inputs with doing sensitivity analysis, that have the least impact were excluded from the modeling process. Accordingly final model has three inputs consists of fuel, animal feed and electricity, and an output consists of the output energy of product. The performance of ANN and ANFIS networks was compared using the coefficient of determination (R 2), the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The results obtained with these performance indices analyses of ANN were compared with those of ANFIS analysis.  Q K k k 2    (d q  z q )  q  1 k  1 2  R 1  Q K   2 z    q q 1 k 1  

RMSE 

1 KQ

MAPE  %  

Q

K

 d

k q

 z qk 

2

q 1 k 1

k k 100 Q K  d q  z q   KQ q 1 k 1  z qk 

   

Where d qk is the kth component of the desired output qth pattern, z qk the kth component of the original unscaled output produced by the network for the qth pattern, Q is the number of patterns in the test set and K is the number of output variables [20].

3.

Results and Discussion

3.1 Estimation of energy consumption The quantity of inputs and output of broiler production and energy equivalent for each of them are given in Table 2. Based on the obtained results the total energy consumption was calculated as 153793.18 MJ (1000 bird) -1; While the total energy output was calculated as 26760.23 MJ (1000 bird) -1. In the last column of Table 2, the share of energy inputs are demonstrated. Accordingly, fuels with a share of 61.48% were the highest energy consumer followed by feed (34.98%) and electricity (3.05%). Similar results were reported by Heidari et al. that highest energy factors were fuel, feed and electricity in broiler production in Yazd province in Iran [10]. In another study, Sefeedpari et al. reported the total energy input was 712,464.83 MJ for poultry farms in Iran and feed and diesel fuel use took the major share of energy use [37].

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Prediction of Output Energy based on Different Energy Inputs on Broiler Production using Application of Adaptive Neural-Fuzzy Inference System Agriculture Science Developments Vol(5), No (2), June, 2016.

Table 2. Energy equivalents of inputs and outputs in broiler production

Items (unit) A. Inputs 1. Human labor (h) 2. Machinery (kg) 3. Fuel (L) 4. Feed (kg) 5. Electricity (kWh) The total energy input (MJ) B. Outputs 1. Broiler (kg)

Quantity per unit (1000 bird)

Total energy equivalent MJ (1000 bird)-1

Percentage (%)

76.59 5.75 1984.35 6674.19 393.39

150.12 304.22 94851.69 53793.98 4693.17 153793.18

0.10 0.20 61.48 34.98 3.05 100

2590.54

26760.23

-

3.2 Evaluation of ANN models In this section, the artificial neural network models were used in order to predict the performance of broilers according to energy consumption inputs for farmers. The used Networks in this research is a multilayer neural network with back-propagation learning method. As previously mentioned, In case of choosing an appropriate structure, i.e. the number of neurons, hidden layers and appropriate activation functions, these networks are able to accurately estimate preferences of each relationship between the input and output. The used Inputs to predict the performance of broiler, using artificial neural network models were the energy inputs of labor, machinery, fuel, feed and electricity per unit in 1000 bird and broiler meat energy was considered as model output. In order to achieve the best structure of a neural network, the different number of structures with one and two layers and with one to 20 number of neurons in the hidden layer has trained and validation test has done. The performance of the best trained network for modeling based on obtained results in this study are shown in Table 3. Based on the results of modeling for 5-12-1 structure with 5 inputs, a hidden layer with 12 neurons and an output layer with one output parameters is the best structure for modeling with neural network. R, RMSE and MAPE for the best network were calculated as 0.81, 0.05 and 0.31, respectively. Table 3. The best result of network performance of boiler production prediction

Output Broiler *

NH* 12

r 0.81

RMSE 0.05

MAPE (%) 0.31

Number of neurons in hidden layer.

Based on the results, which can be seen in Figure 2, correlation between actual and predicted output energy is high meaning that the developed ANN model can predict the amount of output energy on the basis of energy inputs, accurately. Haidari et al. in a study about the modeling for Benefit to Cost Ratio (BCR) on broiler farms in Yazd province by using neural network, were introduced 5-20-1 optimum structure, as the best model [11]. Also, in this study, the values of determination coefficient (R2), MSE and MAPE, were reported 0.978, 0.002 and 2.569, respectively. In another study conducted by Sefeedpari et al. a model based on artificial neural networks was developed to prediction of output energy in poultry for egg production [36]. They reported that the best model was consisted of an input layer with three variables, a hidden layers with thirteen neurons and an output layer with one output variable. The validation results showed that the network yields a high coefficient of determination as 0.992, with minimum mean relative error of 0.0017 and mean absolute percentage error of 1.24%.

Figure 2. Scatter plot of predicted and actual output energy for ANN model

Sama Amid *, Tarahom Mesri Gundoshmian

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Agriculture Science Developments Vol(5), No (2), June, 2016.

3.3 Evaluation of ANFIS models In this part of study, an ANFIS modeling of output energy is carried out based on fuel, feed and electricity energy consumption during broiler production process. Figure 3 shows the structure of best fitted ANFIS network for predicting the performance of broilers based on three high consumption inputs of energy used in the province of Ardabil.

Figure 3. The structure of best fitted ANFIS network

To accurately predict the broiler output energy in the studied region, has been studied Suggested ANFIS model. Specifications and network performance parameters of the suggested model are summarized in Table 4. Various membership functions such as triangular, trapezoidal, Gaussian bell and Gaussian were studied, to achieve the best result. As you can see the combination of bell and linear membership functions for input and output values has the better results than other combinations. The maximum number of membership functions is used in order to achieve the best model. Accordingly, three input membership functions are specified as 3, 3, 3 for models. Also hybrid learning algorithm was selected to identify the relationship between input variables and output. Hybrid algorithm which is a combination of back propagation and least squares method is used to rapidly train and adapt the FIS to determine optimized distribution of MFs. Table 4. The characteristics of the best structure of ANFIS architecture

Item ANFIS

Type of MF Input Output

Number of MF Input Epoch

Gbell

333

Linear

30

Learning method

r

RMSE

MAPE (%)

Hybrid

0.995

0.007

0.046

Performance parameters show that suggested model has the correlation coefficient (r) 0.995, the mean relative error (RMSE) 0.0079 and percent mean absolute error (MAPE) 0.046 to predict performance with more accurate and less error rate. A comparison between the actual and predicted values of output energy after training by ANFIS is shown in Figure 4, which implies that the system is well-trained to model output energy based on actual data of inputs energy consumption.

Figure 4. Scatter plot of predicted and actual output energy for ANFIS model

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Prediction of Output Energy based on Different Energy Inputs on Broiler Production using Application of Adaptive Neural-Fuzzy Inference System Agriculture Science Developments Vol(5), No (2), June, 2016.

Sefeedpari et al. applied an adaptive neural-fuzzy inference system to model output energy on the basis of energies of fossil fuels and electricity inputs [38]. Performance measure in the comparison process between the multivariate linear regression and neuro-fuzzy models was done using root of mean squared error (RMSE). They also came to the conclusion that the neuro-fuzzy model performs slightly better for prediction of energy consumption on dairy farms than the multivariate linear regression model. In another study carried out by Naderloo et al., ANFIS models were developed to forecast the grain yield of irrigated wheat in Abyek town of Ghazvin province, Iran [24]. They reported that R2 and RMSE were calculated as 0.996 and 0.013, respectively. Khoshnevisan et al. developed ANFIS technique with multiple layers more precise than artificial neural network in predicting greenhouse strawberry yield on the basis of different combination of energy inputs in Guilan province of Iran [15]. The R 2, RMSE, MAE and MAPE values for the best ANFIS topology were reported as 0.963, 0.017, 0.014 and 0.003, respectively. Finally, the results showed that ANFIS model can predict strawberry yield relatively better than does ANN model. Figure 5 shown the control level of broiler production obtained from ANFIS. The impact of feed and electricity changing on broiler energy is shows on Figure 5A Broiler energy is shown on vertical axis. As seen on the figure, increasing on feed and electricity values, in a specific range has no effect on the broiler value after that range, with increasing feed value and decreasing electricity value, the broiler energy increases. It is clear that the effect of electricity on broiler energy is more than feed energy. Figure 5B shows the correlation between feed and fuel on broiler energy. The figure indicates that increasing on feed and fuel values, increases the value of broiler energy. According to Figure 5C, the correlation between is a an uneven the two parameters electricity and fuel is an uneven surface, i.e. increasing on fuel and electricity, increases the broiler energy till a specific point, then after leaving that point, the broiler energy decreases. From the figure can deduce that two input parameters (electricity and fuel) almost have the same impact on output value (broiler energy).

(A)

(B)

(C) Figure 5. The obtained images from the controlling rule surfaces of broiler production, the feed-electricity (A), the feed-fuel (B), and the electricity-fuel (C)

3.4 Comparative between ANN and ANFIS model With reference to Table 3 and 4, it can be noted that ANFIS with better results of statistic indices such as higher r and lower RMSE and MAPE, which calculated to be 0.995, 0.0079 and 0.046 respectively, against ANN (0.81, 0.05 and 0.31) was addressed, which implies that the model succeeded in prediction of yield based on energy inputs in comparison with ANN model. The ANN models use crisp values and precise input and output data for modeling but in point of fact, for situations such as production systems especially those related to agricultural systems, data are generally inconstant and imprecise. In this situation, fuzzy set theory has been constructive to quantify the volatile and linguistic data. The results of the present study indicated that ANFIS model was more precise than ANN model for predicting broiler energy on the basis of input energy.

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Agriculture Science Developments Vol(5), No (2), June, 2016.

4.

Conclusion

In this study, ANN and ANFIS models were employed in order to predict energy demand of broiler farms in Iran for the year 2013. The average of total energy inputs and outputs were calculated as 153793.18 MJ (1000bird) -1 and 26760.23 MJ (1000bird)-1 , respectively. Fuel and feed was the most energy consumer in production processing among all input with 61.48% and 34.98%, of total energy use in present farms, respectively. An ANN model was developed based on energy inputs with one hidden layers to predict output energy of broiler production. Accordingly, the best structure of the model was 5-12-1. Moreover, an ANFIS model was employed to forecast output energy of broiler production and compare the results of these two models. The output energy values predicted by ANN and ANFIS models were compared to the actual measured values in order to determine the error of both prediction techniques and validate the results. A comparison between the ANN and neuro-fuzzy models showed that the neuro-fuzzy model performs better. The present study showed that ANFIS is a technique that can be used efficiently to model and predict output energy based on inputs energy consumption. It is believed that this approach can be used to identify many other parameters in different fields.

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Prediction of Output Energy based on Different Energy Inputs on Broiler Production using Application of Adaptive Neural-Fuzzy Inference System Agriculture Science Developments Vol(5), No (2), June, 2016.

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