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Agricultural databases evaluation with machine learning procedure

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Australian Journal of Engineering and Applied Science, 2023

Autour(s)

  • Mahyar Amini and Ali Rahmani

Abstract

This paper reviews our experience with the application of machine learning techniques to agricultural databases. W e have designed and implemented a machine learning workbench, WEKA, which permits rapid experimentation on a given dataset using a variety of machine learning schemes, and has several facilities for interactive investigation of the data: preprocessing attributes, evaluating and comparing the results of different schemes, and designing comparative experiments to be run off-line. We discuss the partnership between agricultural scientist and machine learning researcher that our experience has shown to be vital to success. We review in some detail a particular agricultural application concerned with the culling of dairy herds.

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