CATTLE BODY WEIGHT PREDICTION USING REGRESSION MACHINE LEARNING

Anjar Setiawan; Ema Utami; Dhani  Ariatmanto

doi:10.52436/1.jutif.2024.5.2.1521

Anjar Setiawan Magister of Informatics Engineering, Universitas AMIKOM Yogyakarta, Indonesia
Ema Utami Magister of Informatics Engineering, Universitas AMIKOM Yogyakarta, Indonesia
Dhani Ariatmanto Magister of Informatics Engineering, Universitas AMIKOM Yogyakarta, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.2.1521

Keywords: Cow Weight, Prediction, SVR

Abstract

Increasing efficiency and productivity in the cattle farming industry can have a significant economic impact. Cow health and productivity problems directly impact the quality of the meat and milk produced. In the cattle farming industry, it can help predict cow weight oriented to beef and milk quality. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. This research aims to predict cow weight by increasing the results of smaller MAE values. The methods used are linear Regressor (LR), Random Forest Regressor (RFR), Support Vector Regressor (SVR), K-Neighbors Regressor (KNR), Multi-layer Perceptron Regressor (MLPR), Gradient Boosting Regressor (GBR), Light Gradient boosting (LGB), and extreme gradient boosting regressor (XGBR). Producing cattle weight predictions using the SVR method produces the best values, namely mean absolute error (MAE) of 0.09 kg, mean absolute perception error (MAPE) of 0.02%, root mean square error (RMSE) of 0.08 kg, and R-square of 0.97 compared to with other algorithm methods and the results of statistical correlation analysis showed several significant relationships between morphometric variables and live weight.

Downloads

Download data is not yet available.

References

E. M. M. Van Der Heide, R. F. Veerkamp, M. L. Van Pelt, C. Kamphuis, I. N. Athanasiadis, and B. J. Ducro, “Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle,” Journal of Dairy Science, vol. 102, no. 10, pp. 9409–9421, Oct. 2019, doi: 10.3168/jds.2019-16295.

D. Giannuzzi et al., “Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle,” Journal of Dairy Science, vol. 106, no. 5, pp. 3321–3344, May 2023, doi: 10.3168/jds.2022-22454.

D.-H. Lee et al., “Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network,” Asian-australasian Journal of Animal Sciences, vol. 33, no. 10, pp. 1633–1641, Oct. 2020, doi: 10.5713/ajas.19.0748.

G. Bretschneider, A. Cuatrín, D. Arias, and D. Vottero, “Estimation of body weight by an indirect measurement method in developing replacement Holstein heifers raised on pasture,” Archivos De Medicina Veterinaria, vol. 46, no. 3, pp. 439–443, Jan. 2014, doi: 10.4067/s0301-732x2014000300014.

R. A. Gomes, G. R. Monteiro, G. J. De Freitas Assis, K. C. Busato, M. M. Ladeira, and A. Chizzotti, “Technical note: Estimating body weight and body composition of beef cattle trough digital image analysis1,” Journal of Animal Science, vol. 94, no. 12, pp. 5414–5422, Dec. 2016, doi: 10.2527/jas.2016-0797.

M. Gjergji et al., “Deep Learning Techniques for Beef Cattle Body Weight Prediction,” In 2020 International Joint Conference on Neural Networks (IJCNN), Jul. 2020, doi: 10.1109/ijcnn48605.2020.9207624.

C. G. Dang et al., “Machine Learning-Based live weight Estimation for Hanwoo Cow,” Sustainability, vol. 14, no. 19, p. 12661, Oct. 2022, doi: 10.3390/su141912661.

G. A. Miller, J. J. Hyslop, D. Barclay, A. J. Edwards, W. Thomson, and C.-A. Duthie, “Using 3D imaging and machine learning to predict liveweight and carcass characteristics of live finishing beef cattle,” Frontiers in Sustainable Food Systems, vol. 3, May 2019, doi: 10.3389/fsufs.2019.00030.

J. Wełeszczuk, B. Kosińska-Selbi, and P. Cholewińska, “Prediction of Polish Holstein’s economical index and calving interval using machine learning,” Livestock Science, vol. 264, p. 105039, Oct. 2022, doi: 10.1016/j.livsci.2022.105039.

D. A. Sant’Ana et al., “Weighing live sheep using computer vision techniques and regression machine learning,” Machine Learning With Applications, vol. 5, p. 100076, Sep. 2021, doi: 10.1016/j.mlwa.2021.100076.

Z. E. Huma and F. Iqbal, “Predicting the body weight of Balochi sheep using a machine learning approach,” Turkish Journal of Veterinary & Animal Sciences, vol. 43, no. 4, pp. 500–506, Aug. 2019, doi: 10.3906/vet-1812-23.

A. Ruchay et al., “A comparative study of machine learning methods for predicting live weight of Duroc, Landrace, and Yorkshire pigs,” Animals, vol. 12, no. 9, p. 1152, Apr. 2022, doi: 10.3390/ani12091152.

Y. Cang, H. He, and Y. Qiao, “An intelligent pig weights estimate method based on deep learning in sow stall environments,” IEEE Access, vol. 7, pp. 164867–164875, Jan. 2019, doi: 10.1109/access.2019.2953099.

A. Ibrahim, W. T. Artama, I. G. S. Budisatria, R. Yuniawan, B. A. Atmoko, and R. Widayanti, “Regression model analysis for prediction of body weight from body measurements in female Batur sheep of Banjarnegara District, Indonesia,” Biodiversitas, vol. 22, no. 7, Jun. 2021, doi: 10.13057/biodiv/d220721.

T. K. Ho, “Random decision forests,” N Proceedings of 3rd International Conference on Document Analysis and Recognition, Nov. 2002, doi: 10.1109/icdar.1995.598994.

H. Drucker, C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik, “Support vector regression machines,” Neural Information Processing Systems, vol. 9, pp. 155–161, Dec. 1996, [Online]. Available: https://papers.nips.cc/paper/1238-support-vector-regression-machines.pdf

A. Navot, L. Shpigelman, N. Tishby, and E. Vaadia, “Nearest Neighbor Based Feature Selection for Regression and its Application to Neural Activity,” Neural Information Processing Systems, vol. 18, pp. 996–1002, Dec. 2005, [Online]. Available: https://papers.nips.cc/paper/2848-nearest-neighbor-based-feature-selection-for-regression-and-its-application-to-neural-activity.pdf

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv (Cornell University), Dec. 2014, doi: 10.48550/arxiv.1412.6980.

J. H. Friedman, “Greedy function approximation: A gradient boosting machine.,” Annals of Statistics, vol. 29, no. 5, Oct. 2001, doi: 10.1214/aos/1013203451.

G. Ke et al., “LightGBM: a highly efficient gradient boosting decision tree,” Advances in Neural Information Processing Systems, vol. 30, pp. 3149–3157, Dec. 2017.

T. Chen and C. Guestrin, “XGBoost,” In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, Aug. 2016, doi: 10.1145/2939672.2939785.

A. Ruchay, V. Kober, K. Dorofeev, В. И. Колпаков, A. Gladkov, and H. Guo, “Live weight prediction of cattle based on deep regression of RGB-D images,” Agriculture, vol. 12, no. 11, p. 1794, Oct. 2022, doi: 10.3390/agriculture12111794.

A. Ruchay, V. Kober, K. Dorofeev, В. И. Колпаков, and С. А. Мирошников, “Accurate body measurement of live cattle using three depth cameras and non-rigid 3-D shape recovery,” Computers and Electronics in Agriculture, vol. 179, p. 105821, Dec. 2020, doi: 10.1016/j.compag.2020.105821.

V. A. M. Weber et al., “Cattle weight estimation using active contour models and regression trees Bagging,” Computers and Electronics in Agriculture, vol. 179, p. 105804, Dec. 2020, doi: 10.1016/j.compag.2020.105804.

T. Hastie, R. Tibshirani, and J. H. Friedman, The elements of statistical learning. 2009. doi: 10.1007/978-0-387-84858-7.

L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, Jan. 2001, doi: 10.1023/a:1010933404324.

E. M. M. Van Der Heide, R. F. Veerkamp, M. L. Van Pelt, C. Kamphuis, I. N. Athanasiadis, and B. J. Ducro, “Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle,” Journal of Dairy Science, vol. 102, no. 10, pp. 9409–9421, Oct. 2019, doi: 10.3168/jds.2019-16295.

X. Song, E. A. M. Bokkers, S. Van Mourik, P. W. G. G. Koerkamp, and P. P. J. Van Der Tol, “Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions,” Journal of Dairy Science, vol. 102, no. 5, pp. 4294–4308, May 2019, doi: 10.3168/jds.2018-15238.

G. Sherwin, R. Hyde, M. A. Green, J. Remnant, E. Payne, and P. Down, “Accuracy of heart girth tapes in the estimation of weights of pre‐weaned calves,” Vet Record Open, vol. 8, no. 1, Aug. 2021, doi: 10.1002/vro2.16.

A. Dakhlan, A. Saputra, M. D. I. Hamdani, and S. Sulastri, “Regression Models and Correlation Analysis for Predicting Body Weight of Female Ettawa Grade Goat using its Body Measurements,” Advances in Animal and Veterinary Sciences, vol. 8, no. 11, Jan. 2020, doi: 10.17582/journal.aavs/2020/8.11.1142.1146.

H. Ghotbaldini, M. Mohammadabadi, H. Nezamabadi‐pour, O. Babenko, M. Bushtruk, and S. Tkachenko, “Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed,” Acta Scientiarum. Animal Sciences, vol. 41, no. 1, p. 45282, Jun. 2019, doi: 10.4025/actascianimsci.v41i1.45282.

A. Ibrahim, W. T. Artama, I. G. S. Budisatria, R. Yuniawan, B. A. Atmoko, and R. Widayanti, “Regression model analysis for prediction of body weight from body measurements in female Batur sheep of Banjarnegara District, Indonesia,” Biodiversitas, vol. 22, no. 7, Jun. 2021, doi: 10.13057/biodiv/d220721.

M. Sun, M. Hossain, T. Islam, M. Rahman, M. Hossain, and M. Hashem, “Different body measurement and body weight prediction of jamuna basin sheep in Bangladesh,” SAARC Journal of Agriculture, vol. 18, no. 1, pp. 183–196, Jul. 2020, doi: 10.3329/sja.v18i1.48392.

Z. N. Jannah, B. A. Atmoko, A. Ibrahim, M. A. Harahap, and P. Panjono, “Body weight prediction model analysis based on the body size of female Sakub sheep in Brebes District, Indonesia,” Biodiversitas, vol. 24, no. 7, Jul. 2023, doi: 10.13057/biodiv/d240702.

Y. Cang, H. He, and Y. Qiao, “An intelligent pig weights estimate method based on deep learning in sow stall environments,” IEEE Access, vol. 7, pp. 164867–164875, Jan. 2019, doi: 10.1109/access.2019.2953099.

M. Gjergji et al., “Deep Learning Techniques for Beef Cattle Body Weight Prediction,” In 2020 International Joint Conference on Neural Networks (IJCNN), Jul. 2020, doi: 10.1109/ijcnn48605.2020.9207624.

R. E. Uhrig, ‘‘Introduction to artificial neural networks,’’ Proceedings of IECON '95 - 21st Annual Conference on IEEE Industrial Electronics, Orlando, FL, USA, 1995, pp. 33-37 vol.1, doi: 10.1109/IECON.1995.483329.

C. S. Vui, G. K. Soon, C. K. On, R. Alfred, and P. Anthony, “A review of stock market prediction with Artificial neural network (ANN),” In 2013 IEEE International Conference on Control System, Computing and Engineering, Nov. 2013, doi: 10.1109/iccsce.2013.6720012.

S. Bhardwaj, A. Tarafdar, M. Baghel, T. Dutt, and G. K. Gaur, “Determining Point of Economic Cattle Milk Production through Machine Learning and Evolutionary Algorithm for Enhancing Food Security,” Journal of Food Quality, vol. 2023, pp

M. S. Haris, M. I. Anshori, and A. N. Khudori, “PREDICTION OF STUNTING PREVALENCE IN EAST JAVA PROVINCE WITH RANDOM FOREST ALGORITHM,” Jurnal Teknik Informatika, vol. 4, no. 1, pp. 11–13, Feb. 2023, doi: 10.52436/1.jutif.2023.4.1.614.

D. A. Rachmawati, N. A. Ibadurrahman, J. Zeniarja, and N. Hendriyanto, “IMPLEMENTATION OF THE RANDOM FOREST ALGORITHM IN CLASSIFYING THE ACCURACY OF GRADUATION TIME FOR COMPUTER ENGINEERING STUDENTS AT DIAN NUSWANTORO UNIVERSITY,” Jurnal Teknik Informatika, vol. 4, no. 3, pp. 565–572, Jun. 2023, doi: 10.52436/1.jutif.2023.4.3.920.

C. Cahyaningtyas, D. Manongga, and I. Sembiring, “ALGORITHM COMPARISON AND FEATURE SELECTION FOR CLASSIFICATION OF BROILER CHICKEN HARVEST,” Jurnal Teknik Informatika, vol. 3, no. 6, pp. 1717–1727, Dec. 2022, doi: 10.20884/1.jutif.2022.3.6.493.

D. S. Hamdani, M. Jazman, M. L. Hamzah, and A. Anofrizen, “THE THE EFFECT OF ABDURRAB UNIVERSITY LIBRARY WEBSITE QUALITY ON USER SATISFACTION USING MULTIPLE LINEAR REGRESSION AND IMPORTANCE PERFORMANCE ANALYSIS,” Jurnal Teknik Informatika, vol. 3, no. 6, pp. 1675–1687, Dec. 2022, doi: 10.20884/1.jutif.2022.3.6.454.

R. Kurniawan and R. E. Indrajit, “THE INFLUENCE OF DIGITAL MARKETING AND LIFESTYLE ON THE DECISIONS OF E-COMMERCE (SHOPEE) USERS IN INDUSTRY 4.0,” Jurnal Teknik Informatika, vol. 3, no. 6, pp. 1699–1707, Dec. 2022, doi: 10.20884/1.jutif.2022.3.6.468.

I. Yunanto and S. Yulianto, “TWITTER SENTIMENT ANALYSIS PEDULILINDUNGI APPLICATION USING NAÏVE BAYES AND SUPPORT VECTOR MACHINE,” Jurnal Teknik Informatika, vol. 3, no. 4, pp. 807–814, Aug. 2022, doi: 10.20884/1.jutif.2022.3.4.292.

S. Sumayah, F. Sembiring, and W. Jatmiko, “ANALYSIS OF SENTIMENT OF INDONESIAN COMMUNITY ON METAVERSE USING SUPPORT VECTOR MACHINE ALGORITHM,” Jurnal Teknik Informatika, vol. 4, no. 1, pp. 143–150, Feb. 2023, doi: 10.52436/1.jutif.2023.4.1.417.

L. I. Mustamu and Y. Sibaroni, “FUEL INCREASE SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM AS FEATURE SELECTION,” Jurnal Teknik Informatika, vol. 4, no. 3, pp. 521–528, Jun. 2023, doi: 10.52436/1.jutif.2023.4.3.881.

A. Manik, E. B. Nababan, and T. Tulus, “IMPROVED SUPPORT VECTOR MACHINE PERFORMANCE USING PARTICLE SWARM OPTIMIZATION IN CREDIT RISK CLASSIFICATION,” Jurnal Teknik Informatika, vol. 3, no. 6, pp. 1739–1746, Dec. 2022, doi: 10.20884/1.jutif.2022.3.6.615.

W. Sofiya and E. B. Setiawan, “FINE-GRAINED SENTIMENT ANALYSIS IN SOCIAL MEDIA USING GATED RECURRENT UNIT WITH SUPPORT VECTOR MACHINE,” Jurnal Teknik Informatika, vol. 4, no. 3, pp. 511–519, Jun. 2023, doi: 10.52436/1.jutif.2023.4.3.855..