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Machine Learning Technology and Its Current Implementation in Agriculture

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Information and Communication Technologies for Agriculture—Theme II: Data

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 183))

Abstract

Humans have always been intrigued by the notion that a machine could simulate their brain and mimic their actions. For that reason, through the last decades, artificial intelligence became the most prominent field of computer science, aiming to the development of intelligent machines, which are able complete tasks that require high level of cognition. Artificial Intelligence (AI) is a broad area comprised of advanced mathematical methods and computational techniques, such as machine learning and deep learning. Machine learning refers to the mathematical and algorithmic approaches that enable computers to automatically improve their efficiency in particular tasks, without being explicit programming. By analyzing large amount of data, and recognizing the patterns and structures within, machine learning is enables computers to iteratively learn and improve their efficiency without any human interaction. This chapter aims to an introduction towards understanding what machine learning is, by highlighting its differences with conventional programming and pointing out some of its fundamental features. Moreover, different types of machine learning algorithms are described, and examples are given in order to underline their importance in our everyday lives. Finally, a preliminary scholarly literature survey is presented, indicating studies that are referred in machine learning algorithms in the agricultural domain for the years 2018–2020. The study reveals that machine learning can undoubtedly expand our capabilities in many fields of expertise that affect our lives. Specifically in agriculture, machine learning solutions can improve quality of products and significantly increase operational productivity and efficiency.

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Authors and Affiliations

  1. Institute for Bio-economy and Agri-technology (iBO), Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Athanasios Anagnostis, Gabriela Asiminari, Lefteris Benos & Dionysis D. Bochtis

  2. farmB Digital Agriculture P.C, Thessaloniki, Greece

    Dionysis D. Bochtis

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  1. Athanasios Anagnostis
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  2. Gabriela Asiminari
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  3. Lefteris Benos
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  4. Dionysis D. Bochtis
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Correspondence to Athanasios Anagnostis .

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Editors and Affiliations

  1. Institute for Bio-economy and Agri-technology (iBO), Centre for Research & Technology Hellas (CERTH), Thessaloniki, Greece

    Dionysis D. Bochtis

  2. School of Agriculture, Aristotle Univeristy of Thessaloniki, Thessaloniki, Greece

    Dimitrios E. Moshou

  3. Institute for Bio-economy and Agri-technology (iBO), Centre for Research & Technology Hellas (CERTH), Thessaloniki, Greece

    Giorgos Vasileiadis

  4. Institute for Bio-economy and Agri-technology (iBO), Centre for Research & Technology Hellas (CERTH), Thessaloniki, Greece

    Athanasios Balafoutis

  5. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Anagnostis, A., Asiminari, G., Benos, L., Bochtis, D.D. (2022). Machine Learning Technology and Its Current Implementation in Agriculture. In: Bochtis, D.D., Moshou, D.E., Vasileiadis, G., Balafoutis, A., Pardalos, P.M. (eds) Information and Communication Technologies for Agriculture—Theme II: Data. Springer Optimization and Its Applications, vol 183. Springer, Cham. https://doi.org/10.1007/978-3-030-84148-5_3

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