Applying machine learning to agricultural data

https://doi.org/10.1016/0168-1699(95)98601-9Get rights and content

Abstract

Many techniques have been developed for learning rules and relationships automatically from diverse data sets, to simplify the often tedious and error-prone process of acquiring knowledge from empirical data. While these techniques are plausible, theoretically well-founded, and perform well on more or less artificial test data sets, they depend on their ability to make sense of real-world data. This paper describes a project that is applying a range of machine learning strategies to problems in agriculture and horticulture. We briefly survey some of the techniques emerging from machine learning research, describe a software workbench for experimenting with a variety of techniques on real-world data sets, and describe a case study of dairy herd management in which culling rules were inferred from a medium-sized database of herd information.

References (36)

  • D. Fisher

    Knowledge acquisition via incremental conceptual clustering

    Mach. Learn.

    (1987)
  • B.R. Gaines

    The trade-off between knowledge and data in knowledge acquisition

  • J.H. Gennari

    A survey of clustering methods

  • D. Haussler

    Learning conjunctive concepts in structural domains

  • K.A. Kaufman et al.

    EMERALD 2: an integrated system of machine learning and discovery programs to support education and experimental research

  • Y. Kodratoff et al.

    Building a machine learning toolbox

  • R. Kohavi et al.

    MLC++: a machine learning library in C++

  • M. Lebowitz

    Concept learning in a rich input domain: generalization-based memory

  • Cited by (109)

    • Technological revolutions in smart farming: Current trends, challenges & future directions

      2022, Computers and Electronics in Agriculture
      Citation Excerpt :

      Big data is less about the volume, rather ability to visualize, search, aggregate, massive data sets in reasonable time is required. The different sources of big data, which are widely used are, ground sensors (e.g weather stations and biosensors) (Islam et al., 2021b), historical data collected by governmental and non-governmental agencies (e.g. government reports, statistical, yearbooks and alerts etc.) (McQueen et al., 1995; Tesfaye et al., 2016), web services and online repositories (Becker-Reshef et al., 2010), data gathered from airborne sensors (e.g. satellites, aeroplanes and unmanned aerial vehicles) (Gutiérrez et al., 2008). The data collected from natural hazards, pests attack on crop, disease identification and control using computer vision are also heterogeneous in nature (Sawant et al., 2016).

    View all citing articles on Scopus
    View full text