, regarding the time period of 2004 to 2018, four generic categories were identified ( Figure 1). According to the recent literature survey by Liakos et al. There is a plethora of applications of ML in agriculture. To that end, Machine Learning (ML) has emerged, which is a subset of artificial intelligence, by taking advantage of the exponential computational power capacity growth. The conventional data processing techniques are incapable of meeting the constantly growing demands in the new era of smart farming, which is an important obstacle for extracting valuable information from field data. Nevertheless, big data encompass challenges on account of their so-called “5-V” requirements (a) Volume, (b) Variety, (c) Velocity, (d) Veracity, and (e) Value. The latter has a considerable potential to add value for society, environment, and decision-makers. The large volume of data, which is produced by digital technologies and usually referred to as “big data”, needs large storage capabilities in addition to editing, analyzing, and interpreting. ICT can indicatively include farm management information systems, humidity and soil sensors, accelerometers, wireless sensor networks, cameras, drones, low-cost satellites, online services, and automated guided vehicles. An essential prerequisite of modern agriculture is, definitely, the adoption of Information and Communication Technology (ICT), which is promoted by policy-makers around the world. In general, smart farming is based on four key pillars in order to deal with the increasing needs (a) optimal natural resources’ management, (b) conservation of the ecosystem, (c) development of adequate services, and (d) utilization of modern technologies. This modernization of farming has a great potential to assure sustainability, maximal productivity, and a safe environment. In particular, these two essentials have driven the transformation of agriculture into precision agriculture. As a means of addressing the above issues, placing pressure on the agricultural sector, there exists an urgent necessity for optimizing the effectiveness of agricultural practices by, simultaneously, lessening the environmental burden. Modern agriculture has to cope with several challenges, including the increasing call for food, as a consequence of the global explosion of earth’s population, climate changes, natural resources depletion, alteration of dietary choices, as well as safety and health concerns. General Context of Machine Learning in Agriculture It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.ġ.1. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. Furthermore, crop management was observed to be at the centre of attention. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Only journal papers were considered eligible that were published within 2018–2020. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources.
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