Comparison and Selection of Artificial Intelligence Technology in Predicting Milk Yield
Abstract
Forecasts are an effective decision-making tool, mainly in the dairy industry, because they help improve herd management, save farm energy and optimize long-term capital investment. The application of artificial intelligence technology to predict milk yield is a subject of concern in the scientific community. However, defining a technology or model to predict the effective performance of these products in different environments is a challenging and complex activity, because none of them is accurate in all scenarios. This study compared the application of artificial intelligence technology in milk yield prediction in the literature, and applied analytic hierarchy process to select the most suitable artificial intelligence technology for milk yield prediction. Methods comprehensive analysis, investigation and experiment were used. The results show that the artificial intelligence technology based on artificial neural network is more suitable for the prediction of milk yield than decision tree and support vector machine. In the field of milk production, the most relevant selection criteria are identified as the ability of these technologies to process uncertain data and their ability to obtain accurate results in the best way. The analysis carried out supports the decision-making of milk production organization.
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1. Wen Z, Liao H, Zavadskas EK. Macont: Mixed sggregation by vomprehensive normalization Technique for multi-criteria analysis. Informatica 2020; 31(4): 857–880.
2. Mahmoudi A, Mi X; Liao H, et al. Grey best-worst method for multiple experts multiple criteria de-cision making under uncertainty. Informatica 2020; 31(2): 331–357.
3. Gunnar B. Different Methods to forecast milk delivery to dairy: A comparison for forecasting. International Journal of Agricultural Management 2015; 4(3): 132–140.
4. Yan WJ, Chen X, Akcan O, et al. Big data analytics for empowering milk yield prediction in dairy supply chains. In: International Conference on Big Data (Big Data). Santa Clara, CA, USA: IEEE; 2015. p. 2132–2137.
5. Zhang F, Upton J, Shalloo L, et al. Effect of parity weighting on milk production forecast models. Computers and Electronics in Agriculture 2019; 157: 589–603.
6. Jensen DB, Van Der Voort M, Hogeveen H. Dy-namic forecasting of individual cow milk yield in automatic milking systems. Journal of Dairy Sci-ence 2018; 101(11): 1–12.
7. Da Rosa R, Goldschmidt G, Kunst R, et al. To-wards combining data prediction and internet of things to manage milk production on dairy cows. Computers and Electronics in Agriculture 2020; 169.
8. Nguyen QT, Fouchereau R, Frénod E, et al. Com-parison of forecast models of production of dairy cows combining animal and diet parameters. Computers and Electronics in Agriculture 2020; 170.
9. Perdigón R, González N. A literature review on models to predict milk yields. Revista Ingeniería Agrícola 2020; 10(4): 69–77.
10. Torres-Inga CS, López-Crespo G, Guevara-Viera R, et al. Technical efficiency in dairy farms in the si-erra andina through modeling with neural net-works. Revista Producción Animal 2019; 31(1): 10–15.
11. 11. Kaygisi F, Sezgin FH. Forecasting Goat Milk production in Turkey using artificial neural net-works and box-jenkins models. Animal Review 2017; 4(3): 45–52.
12. González N, Leiva MY, Faggioni KM, et al. Comparative study of Artificial Intelligence tech-niques for the diagnosis of diseases in livestock. Revista de Sistemas, Cibernética e Informática 2018; 15(2): 17–20.
13. 13. Sugiono S, Soenoko R, Riawati L. Investigat-ing the impact of physiological aspect on cow milk production using artificial intelligence. In-ternational Review of Mechanical Engineering 2017; 11(1): 30–36.
14. Gorgulu O. Prediction of 305 days milk yield from early records in dairy cattle using on fuzzy infer-ence system. The Journal of Animal & Plant Sci-ences 2018; 28(4): 996–1001.
15. Machado G, Figueiredoa DM, Resende PC, et al. Predicting first test day milk yield of dairy heifers. Computers and Electronics in Agriculture 2019; 166: 1–8.
16. Liseunea A, Salamone M, Van Den Poel D, et al. Leveraging latent representations for milk yield prediction and interpolation using deep learning. Computers and Electronics in Agriculture 2020; 175:105600.
17. Zhang F, Murphy MD, Shalloo L, et al. An auto-matic model configuration and optimization sys-tem for milk production forecasting. Computers and Electronics in Agriculture 2016; 128: 100–111.
18. Zhang F, Upton J, Shalloo L, et al. Effect of parity weighting on milk production forecast models. Computers and Electronics in Agriculture 2019; 157: 589–603.
19. Zhang W, Yang K, Yu N, et al. Daily milk yield prediction of dairy cows based on the GA-LSTM Algorithm. In: International Conference on Signal Processing (ICSP). Beijing, China: IEEE; 2020. p. 664–668.
20. Saha A, Bhattacharyya S. Artificial insemination for milk production in India: A Statistical Insight. Indian Journal of Animal Sciences 2020; 90(8): 1186–1190.
21. Notte G, Pedemonte M, Cancela H, et al. Resource allocation in pastoral dairy production systems: Evaluating exact and genetic algorithms ap-proaches. Agricultural Systems 2016; 148: 114–123.
22. Eyduran E, Yilmaz I, Tariq M, et al. Estimation of 305-d milk yield using regression tree method in Brown Swiss Cattle. JAPS Journal of Animal and Plant Sciences, 2013, 23(3): p. 731– 735.
23. 23. Piwczyński D, Sitkowska B, Kolenda M, et al. Forecasting the milk yield of cows on farms equipped with automatic milking system with the use of decision trees. Animal Science Journal 2020; 91(1): e13414.
24. Kliś P, Piwczyński D, Sawa A, et al. Prediction of lactational milk yield of cows based on data rec-orded by AMS during the periparturient period. Animals 2021; 11(2):383.
25. Dongre VB, Gandhi RS, Singh A, et al. Compara-tive efficiency of artificial neural networks and multiple linear regression analysis for prediction of first lactation 305-day milk yield in Sahiwal Cattle. Livestock Science 2012; 147(1–3): 192–197.
26. Oscullo J, Haro L. Forecast of the daily demand of the national interconnected system using neural networks. Revista Politécnica 2016; 38 (1): 77–82.
27. Slob N, Catal C, Kassahun A. Application of ma-chine learning to improve dairy farm management: A systematic literature review. Preventive Veteri-nary Medicine 2021; 187: 105237.
28. Perdigón R, Viltres H, Orellana A. Models for predicting perishable products demands in food trading companies. Revista Cubana de Ciencias Informáticas 2020; 14(1): 110–135.
29. Murphy MD, O’mahony MJ, Shalloo L, et al. Comparison of modeling techniques for milk-production forecasting. Journal of Dairy Science 2014; 97(6): 3352–3363.
30. González N, Estrada V, Febles A. Study and selec-tion of artificial intelligence techniques for the diagnosis of diseases. Revista de Ciencias Médi-cas de Pinar del Río 2018, 22(3): 534–544.
31. Saaty TL, Ergu D. When is a decision-making method trustworthy? Criteria for rvaluating mul-ti-vriteria decision-making methods. International Journal of Information Technology & Decision Making 2015; 14(6): 1171–1187.
32. Ossadnik W, Schinke S, Kaspar RH. Group ag-gregation techniques for analytic hierarchy pro-cess and analytic network process: A comparative analysis. Group Decision and Negotiation 2016; 25: 421–457.
33. Rozman Č, Grgić Z, Maksimović A, et al. Multi-ple-criteria approach of evaluation of milk farm models in bosnia and herzegovina. Mljekarstvo 2016; 66(3): 206–214.
34. Montalván-Estrada A, Aguilera-Corrales Y, Ve-itia-Rodríguez E, et al. Multicriteria analysis for theIintegrated management of industrial wastewater. Ingeniería Industrial 2017; 38(2): 56–67.
35. Ocampo CD, Tamayo J, Castaño HM. Risk man-agement in the implementation of photovoltaic systems in gold extraction projects in colombia from the Hierarchical Analysis Process (AHP). Información Tecnológica 2019; 30(3): 127–136.
36. Mendoza A, Solano C, Palencia D, et al. Applica-tion of the Analytical Hierarchy Process (AHP) for decision making with expert judgments. Ingeniare. Revista chilena de ingeniería 2019; 27(3): 348–360.
37. Saaty TL. How to Make A Decision: The analytic hierarchy process. European Journal of Opera-tional Research 1990; 48(1): 926.
38. Liakos KG, Busato P, Moshou D, et al. Machine learning in agriculture: A Review. Sensors 2018; 18: 2674.
DOI: https://doi.org/10.32629/jai.v4i2.502
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