Improving Farm Management Information Systems with Data Mining

Year 2020, , 21 - 30, 31.01.2020

Cagatay Catal Ayalew Kassahun Henk Jan Hoving

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

Abstract

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. 

Keywords

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

References

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