Investigating the Impact of Physiological Aspect on

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International Review of Mechanical Engineering (I.RE.M.E.), Vol. 11, N. 1 ISSN 1970 - 8734 January 2017

Investigating the Impact of Physiological Aspect on Cow Milk Production Using Artificial Intelligence Sugiono Sugiono1, Rudy Soenoko2, Lely Riawati3 Abstract – The purpose of the paper is to investigate the impact of physiological and environmental factors on milk productivity by using artificial intelligence (AI). The model will be useful for the user to decide the best cow treatment in order to gain the best milk production. The research starts with a literature review and an early survey of cattle physiological, environment factors and milk productivity. The next step is measuring the environment data (temperature, wind speed, noise level and relative humidity) and measuring the physiological aspect (heart rate, body temperature) correlated with the milk productivity in 500 pairs of data. All the data are collected and stored into the database and then trained and validated using Back Propagation Neural Network (BPNN) with Genetic Algorithm (GA) optimization. The initial BPNN architectures are selected in 2 hidden layers, delta bar delta learning rule, sigmoid transfer function and epoch 10000. The sensitivity analysis of all independent factors with temperature, relative humidity, core body temperature and heart rate in milk production are successfully presented. Finally, the research successfully increases cow milk production at an average = 0.96 kg/day. Copyright © 2017 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: Cow Physiology, Dairy Cattle, Milk Productivity, Artificial Intelligent


Even these conditions can disrupt the mating process a cow with a success rate only in the range of 10-20% only. J.W. West (2003) in his research entitled "Effect of Heat Stress on Production in Dairy Cattle" stated that the temperature and high relative humidity will lead to high levels of heat stress in dairy cows [4]. High relative humidity values can cause cows difficulty to release heat to the environment with regard to a body temperature that is much hotter than the environment, it will cause a disturbance in appetite/drinking, which in turn lowers milk production. Moreover, West noted that milk production declined as air temperature exceeded 24°C or fell below -12°C. Further, Craig Thomas (2012) in his research on heat stress in cattle stated that heat stress can be valuable strategy in the management of critical empowerment, especially for dairy cows against temperature and relative humidity factor [5]. Holter et al. (1997) reported that heat stress depressed intake of cows more than heifers [6]. Other studies have reported similar results. Jim Reynolds identified the cow comfort into 5 factors like for heat stress, sanitation, free stall design, walking surface and walking distance [7]. In short, dairy facility is an important factor of key success in milk business and it should be designed to keep the cow and calves comfortable in order to maintain milk quality and thus maximize economic production. To manage the right solution to increase milk production, all cow farmers should know the relationship between environment factor and cattle physical factor in milk productivity. It is very difficult to solve the problem using simple mathematics


Milk is a very important food element in the world because it provides calcium, phosphorous, magnesium and protein, that are all essential for human healthy [1]. An adequate consumption of milk from the early childhood and throughout all life can help to make bones stronger and to protect them against various diseases in their life. Milk can be directly consumed by human or in the other forms of products such as cheese, ice cream, butter, ghee, cream, yoghurt, etc. Dairy cattle in Indonesia has generally a minor production of milk compared to animal husbandry in sub–tropical countries like Australia, USA and Europe. This is not only caused by the quality of food, but also by the environment condition and design facility. The characteristic of tropical weather with higher relative humidity and warm temperature creates a trend to increase the heat stress as indicated by increasing heart rate, blood pressure and metabolism. Moreover, currently dairy house design does not consider the needs of physic and psychology of dairy cattle. Michel J. Brouk et al. in their research suggested that due to the increasing milk production, facility and climate effect should be a priority program in a better dairy cow management [2]. Animal science instructors, Adrew P. Fidler and Karl Van Devender (2011) in their article stated that during the summer in Arkansas comfortable environmental conditions that can cause milk production of dairy cows has fallen dramatically with the average productivity at only 50% [3].

Copyright © 2017 Praise Worthy Prize S.r.l. - All rights reserved

DOI: 10.15866/ireme.v11i1.9873


Sugiono Sugiono, Rudy Soenoko, Lely Riawati

modeling as too many factors are involved in cow milk productivity. A neural network (NN), which imitates the human brain in problem solving, is a more general approach to handle this type of problem. Hence, the research attempt is to build an adaptive system (BPNN) to predict the performance of dairy cattle based on environmental and physical data. According to NN model, the user can easily select the best cow treatment to get the best cow performance.


Research Materials


Physical of Dairy Cattle

The most common neural network model is the multilayer perceptron (MLP). This type of neural network is known as a supervised network because it requires a desired output in order to learn. The goal of this type of network is to create a model that could correctly map the input to the output using historical data so that the model can then be used to produce the output when the desired output is unknown. The MLP and many other neural networks learn using an algorithm called “back-propagation”. With the backpropagation neural network (BPNN), the input data is repeatedly presented to the neural network. With each presentation the output of the neural network is compared to the desired output and an error is computed. This error is then feedback (back-propagated) to the neural network and used to adjust the weights matrix such that the error decreases with each iteration, the neural model gets increasingly closer value to producing the desired output (see Fig. 1). This process is known as "training". Back propagation has been used since the 1980s to adjust the weights of the network. The principle of BPNN is to calculate the error by taking the difference between the calculating result and the actual result, then the error is fed back through the network and the weights are adjusted to minimize the error. Weigh adaptation in BPNN is generally divided into two main kinds of a clash global algorithm (example: conjugate gradient and steepest decent) and a clash local algorithm (examples: delta bar delta and Quick-prop). Local adaptation strategies can be found at simulated annealing algorithm and genetic algorithm (GA). Deltabar-Delta is one of the heuristic algorithms for adjusting the learning rate with involving additive constant, multiplicative constant and smoothing factor. Delta-bar-Delta is inspired by the observation that the error surface may have a different gradient along each weight direction, and that consequently each weight should have its own learning rate (i.e. step size). Weights are updated using the same formula as in conjugate gradient decent, except that each weight has its own time-dependent learning rate. Delta-Bar-Delta applies four heuristics regarding gradient decent [11]:  Every weight should have its own individual learning rate.  Every individual learning rate should adjust over time.  If the error derivative has the same sign for several consecutive steps, then the learning rate increases.

Dairy cattle generally has a physical characteristic of mammary glands is larger than cow's beef cattle. Dairy cows is only benefitted for producing milk as maximum as possible. The best cattle is indicated by a higher productivity, long-term productivity and high quality of milk. There are several factors that influence the total of milk production such us environment, species (breeds), individual factors, genetic, common factors, physiological factors, etc. When the environment temperature increases, cow body attempts to regulate the core body temperature by the altering the physiological and metabolic function. Dairy cattle are particularly more susceptible to increased ambient temperature than other ruminants because of their high metabolic rate and of poor water retention mechanism in kidney and gastrointestinal tract [8]. Naturally, environment condition will physiologically impact the dairy cattle as heart rate, blood pressure, respiration rate, core body temperature and metabolism. Cattle body endurance to physical change is depending on some factors e.g. age, weight and gen. II.2.

Neural Network (NN)

A Neural Network can be described as a “black box” that knows how to process inputs system to create useful outputs. The NN calculation is very complex and difficult to understand by using a mathematical model. Neural network copied the working system of biological nervous system as an example of the brain for processing the information. The goal of a neural network is to minimize the error which is indicated by mean square error (MSE). The other expert defines the neural network (NN) as a powerful data modeling tool that is able to capture and represent complex input/output relationships involving many experiment factors [9]. The advantage of NN tool is to recognize a lot of problems in the science area, medicine, military, financial, etc. to perform finishing tasks like the human brain. Neural network software is employed to simulate research, build and apply artificial neural networks, biological neural networks and in some cases a wider array of adaptive systems. Much NN software provides a graphical neural network development tool that enables the user to easily create a neural network model for the data experiment.

Fig. 1. Description of how the BPNN is working to train the data sets [10]

Copyright © 2017 Praise Worthy Prize S.r.l. - All rights reserved

International Review of Mechanical Engineering, Vol. 11, N. 1


Sugiono Sugiono, Rudy Soenoko, Lely Riawati


 When the sign changes alternatively over a number of steps, then there is a decrease of the learning rate: clearly the large rate causes oscillations. Weights that are updated have resemblance method with back propagation, except that the momentum parameter is not used and each weight has its own time dependent learning rate.

Input Data No Te (oC) 1 2 3 499 500

III. Research Methodology According to the goals of the research, there are two main activities for analysing the environmental/ physiological factors on milk production and air conditioning (water treatment) to increase milk production. To study the sensitivity analysis, collecting – developing the database and BPNN training are developed. The database is designed into two sections of input data and output or desired data. All the data are collected based on cattle production with an age between 2 to 6 years old for Holstein Friesian were marked by white and black spot or red spot. There are some equipments to support the research for collecting data that are ruler, velometer, infrared thermometer, stethoscope, stopwatch, and camera (see Figs. 2(a) and (b)). The velometer is used to measure data of wind speed, environment temperature and relative humidity. The infrared thermometer is employed to look for the cow body temperature by scanning inside the cow ear at around 1 minute. The object of the study is the dairy cattle in Tepas region, Blitar, Indonesia. The observations have been done for 2 months (May, June 2015) in three time sections for morning, Afternoon and evening. Table I shows some of the database of environment factor, physical condition and correlated with milk productivity in 500 pairs data collection. The input data contains 6 factors which affected the milk production. The input factors are environment temperature (Te), relative humidity (RH), air speed (V), oxygen proportion (O2), heart rate (bpm), and cow body temperature (Tc). As example, first data has input environment temperature is Te = 27.90 oC, relative humidity RH = 65.90 CM, air velocity = 0.04 m/s, Oxygen O2 = 19.50%, cow heart rate HR = 61.5 bpm, core body temperature Tc = 38.3oC will produce milk = 8.9 liter per day.


27.90 28.80 29.70 33.40 33.00

RH (%) 65.90 76.20 72.80 64.20 64.10

V (m/s) 0.040 0.070 0.070 0.030 0.020

O2 (ppm) 19.50 20.80 19.40 19.60 19.60

HR (bpm) 61.5 63.3 65.1 72.5 71.7

Tc (oC) 38.3 37.0 37.2 37.9 38.0

Output Data Milk (Liter) 8.9 7.3 7.2 6.5 6.6

Fig. 3 explains in details all the steps of the project searching for the correlation of environment factor, physiological factor and milk production. The BPNN will train the database with GA optimisation for number of neurons from the first hidden layer and the second hidden layer. The number of neurons from the first hidden layer is 20 neurons while for the second hidden layer is 10 neurons. Mean square error (MSE) is employed to evaluate the training quality until convergence solution is achieved. The others Delta-Bar-Delta parameters are Sigmoid transfer function from input to hidden layer , Sigmoid transfer function from first hidden layer to second hidden layer, Linier Sigmoid transfer function from second hidden layer to desire, 70% of database is used for training, 20% database is used for validation and 10% of databased is used for testing. On the other hand, Genetic Algorithm has the following parameter as: Number of epochs = 1000, Population size = 50, Maximum generation = 100, Maximum evolution time = 60, Selection basis rank = roulette, Crossover = one point, Mutaion = heuristic denan probabilitas = 0.01. The next step of the research is to test the BPNN model in any input variances. Test stage is very important to look for the best scenario for to increase milk production. Finally, the last step of the research is to analyze the sensitivity of input factor correlated with the impact in cow milk productivity. The result of sensitivity analysis is important to the future for the treatment to increase the milk production.



The outcome of the research is firstly indicated by collecting the data to put into the database (Table I). According to statistical test, all the input data are categorized in normal distribution and sufficient number of data to do the next action. More in the details of the collecting data, the temperature has range between 27 – 34 °C (average = 30.52 °C), relative humidity has range between 58 – 86 % (average 72%), etc. To do the BPNN training, all the input and desire data are assigned in BPNN structure with GA to optimise the number of neurons in first and second hidden layer and to optimise the network weights. The selection of connection weights in the neural network is a key issue in BPNN performance.


Figs. 2. (a) Velometer to measure air velocity, wind speed and relative humidity, (b). Infrared thermometer to measure cow body temperature

Copyright © 2017 Praise Worthy Prize S.r.l. - All rights reserved

International Review of Mechanical Engineering, Vol. 11, N. 1


Sugiono Sugiono, Rudy Soenoko, Lely Riawati


Early Survey

Collecting Data: Database

Assign Database to BPNN

Fig. 4. Optimising BPNN training process using genetic algorithm (GA)

BPNN Structure

BPNN Training

MSE Convergence?

Finally, the BPNN model is ready to be used for predicting any input data to look for the output data (milk production, litter). As example, the input data of environment factor (Te = 27°C, RH = 67%, v = 0.025 m/s, O2 = 20.50 ppm) and cow physical data (Tc = 37.5°C, HR = 65 bpm) will produce milk = 11.1 litter. The BPNN model is very useful for farmers or engineers to do some test looking for looking for the best place for breed, maintain cattle house, and finance analysing. To understand the impact of dependent variable (environment condition and cow physical factor) in independent variable (milk productivity), the sensitivity analysis is employed in this research under a given set of assumptions. This technique is used within specific boundaries that will depend on one input variable; the other input variable is constant, such as the effect of environment temperature (Te) in milk production. As example, to look for the sensitivity of environment temperature Te, the other input data are set in commonly (average) condition. By using the trained database, BPNN model can predict easily the amount of milk production in litter of volume. Fig. 5(a) shows the sensitivity graph of environment temperature toward milk production. According to the graph, it can be seen that milk productivity gradually decrease coinciding with increasing temperature from 20°C to 36°C. The best selection of trend line is in polynomial graph in order 5 with R2 = 0.997 and Y = 0.000x5 - 0.019x4 + 0.237x3 - 1.281x2 + 2.582x + 13.30. Fig. 5(b) explains the sensitivity graph of cow heart rate to milk production. Trend of the graph is categorized in polynomial order 3 with formula y = -0.011x3 - 0.176x2 + 0.706x + 14.48 and R² = 0.994. Fig. 5(c) describes the sensitivity graph of cow body temperature toward milk production. Trend of the graph is polynomial order 4 with formula y = 0.084x4 + 1.245x3 - 6.85x2 + 16.82x - 0.342 and grafic quality is at R² = 0.996. Fig. 5(d) explains the sensitivity graph of cow relative humidity to milk production. Trend of the graph is polynomial order 3 with formula y = 0.049x3 + 0.346x2 + 0.032x + 12.40 and value R² = 0.975. All the above sentivity anayisis are categorised in strong value.

Genetic Algorithm




Sensitivity Analysis


Fig. 3. Database training using BPNN – Genetic Algorithm (GA) model

The complex network connection will degrade BPNN performance to find the global minima. The randomization method is commonly used to initialize the network weights before training. Genetic algorithm (GA) is employed to minimize fitness criteria (MSE) by BPNN weights adjustment. The main advantage of using GA is associated with its ability to automatically discover a new value of neural network parameters from the initial value. There are some GA parameters that are employed in this BPNN training: This study selected fitness convergence that the BPNN training will stop the evolution when the fitness is deemed as converged. The Roulette rule is employed to select the best chromosome based on proportionality to its rank. The initial values for learning rate and momentum are 0.500000 and 0.0166. Number of population is 50 chromosomes and generation number for maximum 100. Initial network weight factor is 0.1074. Mutation probability is 0.01. Crossover will combine two chromosomes (50% from parent 1 and 50% from parent 2) to generate new chromosome (offspring). Fig. 4 shows the BPNN training process with the best mean square error MSE = 0.0035 in fifth replication and convergence in ≈ 25 epoch. Copyright © 2017 Praise Worthy Prize S.r.l. - All rights reserved

International Review of Mechanical Engineering, Vol. 11, N. 1


Sugiono Sugiono, Rudy Soenoko, Lely Riawati





Figs. 5. Sensitivity analysis for: (a) environment temperature, (b) heart rate, (c) cow body temperature, (d) relative humidity

It means that small modification of environment temperature (Te), relative humidity (RH), heart rate (bpm), and cow body temperature (Tc) will give big influence in milk production. The others sensitivity analysis of wind speed and oxygen proportion in air are categorized in low sensitivity. Based on the sensitivity analysis, water sprinklers on the cow body are an aproprite decision to controll humidity. Water sprinklers will reduce the environmet temperature, cow body tempereature and heart rate/respiration rate. On the other hand, it will incerase the amount of water vapour on air (increasing relative humidity). As consequence, air circulation is an important part in the air conditioning. Fig. 6(a) shows the thermal imaging of the whole cow body themperature. The red colour is the hottest temperature and the blue colour is the lowest temperature. As in the picture, there are 4 regions in higher temperature than other cow parts. The hot regions are leg, tail, stomach and neck which are correlated with cow activities. Fig. 6(b) explains the 3 important regions potition of cow body region that will be soaked by water. The tail region is negleted in the treatment as small part and sensitive part of cow body. According to direct observation, the best intermiten spray was at interval of 1.30 hour. There are two experiments to explain the impact of water treatment.



Figs. 6. (a) thermal imaging on cow body, (b) Tree regions of cow water treatment

The first treatment is measuring production of 5 different cattles in 20 days without water spray while the second treatment used water spray on cattle body. Overall, the comparison of milk production with teratment and without treatment is explained in Fig. 7. According to the graph, it can be seen that water spray can increase milk production from an avarage 11.54 kg/day to 12.50 kg/day or increase = 0.96 kg/day (8.31%).



The advantage of the BPNN model is that users do not need to measure or to do more experiment, but they are just only running the system to get information of cow milk productivity from any kinds of input data for

Copyright © 2017 Praise Worthy Prize S.r.l. - All rights reserved

International Review of Mechanical Engineering, Vol. 11, N. 1


Sugiono Sugiono, Rudy Soenoko, Lely Riawati

environment temperature, wind speed, relative humidity, oxygen proportion in air, cow body temperature and heart rate. According to the sensitivity analysis, the strength factors influencing milk production are core body temperature, environment temperature, relative humidity and heart rate. All these factors generate the dairy cattle heat stress, which will decrease the milk production as presented by BPNN model.



Firstly, the paper contributes to understand the relationship between physiology, environment and milk productivity by using artificial intelligence (AI). Secondly, the paper presents profits of using GA to optimize NN performance with adjusting the Delta- BarDelta learning parameters of learning rate, additive, multiplicative and smoothing constant. According to the result, learning rule using GA application gave MSE < 0.0035. Finally, the paper proposes how to evaluate the best cow management for increasing milk productivity by reducing sensitive factor of cow body temperature, environment temperature, heart rate and relative humidity.

Acknowledgements Thanks to the Ministry of National Education of the Republic of Indonesia for supporting this paper. The authors are also grateful to the Industrial Engineering Department, the Brawijaya University, Malang Indonesia for their extraordinary courage. Fig. 7. Comparison the impact of water spray in milk production


There are some strategies to increase milk production of dairy cattle as the impact of heat stress (high temperature and high humidity). Some recommendations to eliminate the effect of heat stress are:  To plant some trees surrounding the dairy cattle house to make natural shade. Trees are excellent source of shade and effective to block direct solar radiation. Moreover, trees photosynthesis can reduce heat energy and produce cold moisture. As a consequence, cow thermal comfort will better than without tress near the cow house.  The second recommendation to reduce heat stress economically is fan installation. Fan works to circulate fresh air (lower relative humidity and temperature) inside the cow house. In addition, fan can remove moisture produced by cow body and others source got out by the cow house. The fan specification and fan position determine the level of success for reducing heat stress.  Water sparkling treatment to reduce cow body temperature.  Selecting the best roof material to absorb heat transfer from sun light. For future action, to get more accurate and more complete information, The BPNN model should be maintained as:  Updating more data collection  Comparing the delta bar delta learning algorithm with the others training algorithm such as momentum, Quickprop, and levemberg Marquardt  Supposing to include the other factors that could be impact the milk productivity, e.g. age, weight, daily metabolism, etc.



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International Review of Mechanical Engineering, Vol. 11, N. 1


Sugiono Sugiono, Rudy Soenoko, Lely Riawati

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Authors’ information 1 Lecturer at The Brawijaya University of Industrial Engineering Dept. (currently); worked at PT. Surveyor Indonesia (consultant) and PT. Mitra Saruta Indonesia (Textile). E-mail: mail: [email protected] [email protected] 2 Head of Doctoral program at The Brawijaya University of Mechanical Engineering Dept. (currently) E-mail: mail: [email protected] 3

Lecturer at The Brawijaya University of Industrial Engineering Dept. (currently); worked at PT. ECCO Indonesia. E-mail: mail: [email protected] Sugiono, Bachelor degree in Mechanical Engineering Department, The University of Brawijaya (UB) – Indonesia, Master degree in Industrial Engineering Department at the 10 November institute of Technology, Indonesia and PhD in Art, Design &Technology, University of Derby, UK (2012). One of his interests is ergonomics especially the microclimate ergonomics and psychology ergonomics. He is also interested in artificial intelligence of product design and biomechatronic – sound therapy. Rudy Soenoko, Bachelor degree in Mechanical Engineering Department, The University of Brawijaya (UB) – Indonesia, Master degree in Mechanical Engineering Depa Department rtment at the University Melbourne, Australia and Doctoral Degree in Medicine Faculty, University of Brawijaya. One of his interests is fluid mechanic, material and thermal, etc. Lely Riawati, Bachelor degree in Chemical Engineering Department, Sepuluh Nopember Institute of Technology (ITS) – Indonesia, Master degree in Mechanical Engineering Major Industrial Management &Engineering Department at The University of Brawijaya. Her interests arein Quality Engineering as well as in Safety, Health and Enviro Environment nment.

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International Review of Mechanical Engineering, Vol. 11, N. 1


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