Machine Learning Technology and Its Current Implementation in Agriculture

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.