Improving Farm Management Information Systems with Data Mining

Yıl 2020, , 21 - 30, 31.01.2020

Cagatay Catal , Ayalew Kassahun Henk Jan Hoving

https://doi.org/10.17694/bajece.555680

Öz

Over the past several
years, farm enterprises have grown in size substantially while their number has
steadily declined. As the size of their farms grow more and more farmers are
deploying information systems, commonly called as Farm Management Information
Systems (FMIS), to manage the day to day activities of their farms. The
deployment of FMIS enable farmers to capture detailed data that can potentially
be analysed by data mining tools to provide valuable information for optimizing
the farm enterprises. However, data mining is generally not a common feature of
many FMIS. In order to evaluate the suitability of data mining for use in FMIS,
we performed two case studies using data captured in FMIS and applying data
mining. Microsoft Azure Machine Learning Studio is chosen because it provides a
simple drag-and-drop visual interface that can be used by farm domain experts.
We addressed two common problems in dairy farming: calving prediction of dairy
cows and prediction of lactation value of milking cows. In both cases we built
data mining models and run experiments and our results in both cases indicate
that the required data is available from FMIS and data mining techniques provides
acceptable performance. We also showed that farm domain experts can easily use
a user-friendly and drag-and-drop data mining tools with minimal initial
training. Based on the insight from the two case studies and literature study, we
identified several decision problems that can be addressed with data mining such
as heat prediction and  lameness prediction. 

Anahtar Kelimeler

Farm management information systems, machine learning, calving prediction, lactation prediction

Kaynakça

• [1] Albersmeier, F., Schulze, H., Jahn, G., & Spiller, A. (2009). The reliability of third-party certification in the food chain: From checklists to risk-oriented auditing. Food Control, 20(10), 927-935. doi:https://doi.org/10.1016/j.foodcont.2009.01.010[2] Alsaaod, M., Römer, C., Kleinmanns, J., Hendriksen, K., Rose-Meierhöfer, S., Plümer, L., & Büscher, W. (2012). Electronic detection of lameness in dairy cows through measuring pedometric activity and lying behavior. Applied Animal Behaviour Science, 142(3), 134-141. doi:10.1016/j.applanim.2012.10.001[3] Azzaro, G., Caccamo, M., Ferguson, J. D., Battiato, S., Farinella, G. M., Guarnera, G. C., . . . Licitra, G. (2011). Objective estimation of body condition score by modeling cow body shape from digital images. J Dairy Sci, 94(4), 2126-2137. doi:10.3168/jds.2010-3467[4] Borchers, M. R., Chang, Y. M., Proudfoot, K. L., Wadsworth, B. A., Stone, A. E., & Bewley, J. M. (2017). Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle. J Dairy Sci, 100(7), 5664-5674. doi:10.3168/jds.2016-11526[5] Caraviello, D. Z., Weigel, K. A., Craven, M., Gianola, D., Cook, N. B., Nordlund, K. V., . . . Wiltbank, M. C. (2006). Analysis of Reproductive Performance of Lactating Cows on Large Dairy Farms Using Machine Learning Algorithms. J Dairy Sci, 89(12), 4703-4722. doi:10.3168/jds.S0022-0302(06)72521-8[6] Choubey, M. K. (2011). IT Infrastructure and Management (For the GBTU and MMTU): Pearson Education India.[7] Chung, Y., Lee, J., Oh, S., Park, D., Chang, H., & Kim, S. (2013). Automatic detection of cow’s oestrus in audio surveillance system. Asian-Australasian journal of animal sciences, 26(7), 1030. [8] Cook, T. D., Campbell, D. T., & Day, A. (1979). Quasi-experimentation: Design & analysis issues for field settings (Vol. 351): Houghton Mifflin Boston.[9] Delaval. (14-3-2018a). BCS [10] Delaval. (14-3-2018b). View BCS. [11] Delta, C. (February 1998). lactatieproductie en 305 dagenproductie. Handboek NRS. [12] Fenlon, C., O’Grady, L., Doherty, M., Butler, S., Shalloo, L., & Dunnion, J. (2016, 12-15 Dec. 2016). Regression Techniques for Modelling Conception in Seasonally Calving Dairy Cows. Paper presented at the 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).[13] Grzesiak, W., Błaszczyk, P., & Lacroix, R. (2006). Methods of predicting milk yield in dairy cows—Predictive capabilities of Wood's lactation curve and artificial neural networks (ANNs). Computers and Electronics in Agriculture, 54(2), 69-83. [14] Hady, M. F. A., & Schwenker, F. (2013). Semi-supervised Learning. In M. Bianchini, M. Maggini, & L. C. Jain (Eds.), Handbook on Neural Information Processing (pp. 215-239). Berlin, Heidelberg: Springer Berlin Heidelberg.[15] Hastie, T., Tibshirani, R., & Friedman, J. (2009). Unsupervised learning The elements of statistical learning (pp. 485-585): Springer.[16] Hosseini, S., Turhan, B., & Mäntylä, M. (2017). A benchmark study on the effectiveness of search-based data selection and feature selection for cross project defect prediction. Information and Software Technology. [17] Husemann, C., & Novković, N. (2014). FARM MANAGEMENT INFORMATION SYSTEMS: A CASE STUDY ON A GERMAN MULTIFUNCTIONAL FARM. [18] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2017). An Introduction to Statistical Learning with Applications in R. doi:DOI 10.1007/978-1-4614-7138-7[19] Kamphuis, C., Mollenhorst, H., Feelders, A., Pietersma, D., & Hogeveen, H. (2010). Decision-tree induction to detect clinical mastitis with automatic milking. Computers and Electronics in Agriculture, 70(1), 60-68. doi:https://doi.org/10.1016/j.compag.2009.08.012[20] Kim, C.-H., Weston, R. H., Hodgson, A., & Lee, K.-H. (2003). The complementary use of IDEF and UML modelling approaches. Computers in Industry, 50(1), 35-56. [21] Kim, T., & Heald, C. W. (1999). Inducing inference rules for the classification of bovine mastitis. Computers and Electronics in Agriculture, 23(1), 27-42. doi:https://doi.org/10.1016/S0168-1699(99)00003-4[22] Mahmoud, F., Christopher, B., Maher, A., Jürg, H., Alexander, S., Adrian, S., & Gaby, H. (2017). Prediction of calving time in dairy cattle. Animal Reproduction Science, 187, 37-46. doi:https://doi.org/10.1016/j.anireprosci.2017.10.003[23] Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.[24] Nikkilä, R., Seilonen, I., & Koskinen, K. (2010). Software architecture for farm management information systems in precision agriculture. Computers and Electronics in Agriculture, 70(2), 328-336. doi:https://doi.org/10.1016/j.compag.2009.08.013[25] Paraforos, D. S., Vassiliadis, V., Kortenbruck, D., Stamkopoulos, K., Ziogas, V., Sapounas, A. A., & Griepentrog, H. W. (2017). Multi-level automation of farm management information systems. Computers and Electronics in Agriculture, 142, 504-514. doi:https://doi.org/10.1016/j.compag.2017.11.022[26] Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking: " O'Reilly Media, Inc.".[27] Rahman, A., Smith, D. V., Little, B., Ingham, A. B., Greenwood, P. L., & Bishop-Hurley, G. J. (2018). Cattle behaviour classification from collar, halter, and ear tag sensors. Information Processing in Agriculture, 5(1), 124-133. doi:https://doi.org/10.1016/j.inpa.2017.10.001[28] Rutten, C. J., Steeneveld, W., Vernooij, J. C. M., Huijps, K., Nielen, M., & Hogeveen, H. (2016). A prognostic model to predict the success of artificial insemination in dairy cows based on readily available data. J Dairy Sci, 99(8), 6764-6779. doi:10.3168/jds.2016-10935[29] Shahinfar, S., Page, D., Guenther, J., Cabrera, V., Fricke, P., & Weigel, K. (2014). Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms. J Dairy Sci, 97(2), 731-742. doi:10.3168/jds.2013-6693[30] Shahriar, M. S., Smith, D., Rahman, A., Freeman, M., Hills, J., Rawnsley, R., . . . Bishop-Hurley, G. (2016). Detecting heat events in dairy cows using accelerometers and unsupervised learning. Computers and Electronics in Agriculture, 128, 20-26. doi:https://doi.org/10.1016/j.compag.2016.08.009[31] Šmite, D., Wohlin, C., Gorschek, T., & Feldt, R. (2010). Empirical evidence in global software engineering: a systematic review. Empirical software engineering, 15(1), 91-118. [32] Sørensen, C. G., Pesonen, L., Bochtis, D. D., Vougioukas, S. G., & Suomi, P. (2011). Functional requirements for a future farm management information system. Computers and Electronics in Agriculture, 76(2), 266-276. doi:https://doi.org/10.1016/j.compag.2011.02.005[33] Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction (Vol. 1): MIT press Cambridge.[34] Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017). Applications of machine learning in animal behaviour studies. Animal Behaviour, 124, 203-220. [35] Vanrell, S. R., Chelotti, J. O., Galli, J., Rufiner, H. L., & Milone, D. H. (2014). 3d acceleration for heat detection in dairy cows. Paper presented at the XLIII Jornadas Argentinas de Informática e Investigación Operativa (43JAIIO)-VI Congreso Argentino de AgroInformática (CAI)(Buenos Aires, 2014).[36] Wohlin, C., Runeson, P., Host, M., Ohlsson, M., Regnell, B., & Wesslen, A. (2000). Experimentation in software engineering: an introduction. 2000: Kluwer Academic Publishers.[37] Yazdanbakhsh, O., Zhou, Y., & Dick, S. (2017). An intelligent system for livestock disease surveillance. Information Sciences, 378, 26-47. doi:https://doi.org/10.1016/j.ins.2016.10.026[38] Zaborski, D., Proskura, W. S., Grzesiak, W., Szatkowska, I., & Jędrzejczak-Silicka, M. (2017). Use of random forest for dystocia detection in dairy cattle. Applied Agricultural and Forestry Research, 147. [39] Tummers, J., Kassahun, A., & Tekinerdogan, B. (2019). Obstacles and features of Farm Management Information Systems: A systematic literature review. Computers and Electronics in Agriculture, 157, 189-204.