Mobile robotics in smart farming: current trends and applications

Abstract

In recent years, precision agriculture and smart farming have been deployed by leaps and bounds as arable land has become increasingly scarce. According to the Food and Agriculture Organization (FAO), by the year 2050, farming in the world should grow by about one-third above current levels. Therefore, farmers have intensively used fertilizers to promote crop growth and yields, which has adversely affected the nutritional improvement of foodstuffs. To address challenges related to productivity, environmental impact, food safety, crop losses, and sustainability, mobile robots in agriculture have proliferated, integrating mainly path planning and crop information gathering processes. Current agricultural robotic systems are large in size and cost because they use a computer as a server and mobile robots as clients. This article reviews the use of mobile robotics in farming to reduce costs, reduce environmental impact, and optimize harvests. The current status of mobile robotics, the technologies employed, the algorithms applied, and the relevant results obtained in smart farming are established. Finally, challenges to be faced in new smart farming techniques are also presented: environmental conditions, implementation costs, technical requirements, process automation, connectivity, and processing potential. As part of the contributions of this article, it was possible to conclude that the leading technologies for the implementation of smart farming are as follows: the Internet of Things (IoT), mobile robotics, artificial intelligence, artificial vision, multi-objective control, and big data. One technological solution that could be implemented is developing a fully autonomous, low-cost agricultural mobile robotic system that does not depend on a server.

Integration of Technology in Agricultural Practices towards Agricultural Sustainability: A Case Study of Greece

“…Furthermore, an adoption category based on the innovation decision period has been highlighted by Yépez-Ponce et al ( 2021) [22]. The amount of time needed to complete the innovation-decision process is known as the innovation-decision period.…”

“…Furthermore, the steps of the decisionmaking process are shown by Rogers' (1983) [25] innovation decision model [26][27][28]. These stages include learning about an innovation for the first time, deciding whether to accept or reject it, putting the new concept into practice if it is accepted, and verifying this choice [22].…”

“…Achieving economic viability might also include taking steps like cutting down on practices that could endanger the environment, the health of farmers, and the welfare of customers. Instead, farmers would depend on the particulars of the production system, the cost of the equipment, chemical fertilizer, and pesticides (for farmers who can afford these inputs) [22]. Of course, a variety of other elements, such as household qualities like managerial skill, institutions, infrastructure, and market access, among others, influence economic sustainability in addition to crop-production techniques [8,10,74,75].…”

Power Consumption Analysis of a Prototype Lightweight Autonomous Electric Cargo Robot in Agricultural Field Operation Scenarios

“…Despite the aforementioned benefits of investigating the power characteristics of the electric agricultural robots, little progress has been made toward this direction and priority shifting is required. Indeed, numerous studies are directed towards the potential of enhancing the intelligence of robotic vehicles to perform agricultural operations, mainly focusing on (or improving) a single characteristic [43,44]. The examination of literature reviews dedicated to agricultural robotic vehicles [12,27,[43][44][45] reflects the focus of the researchers on improving the intelligence of robots, but it also verifies that only a limited number of works address the energy performance assessment of the robotic platforms [46][47][48].…”

“…Indeed, numerous studies are directed towards the potential of enhancing the intelligence of robotic vehicles to perform agricultural operations, mainly focusing on (or improving) a single characteristic [43,44]. The examination of literature reviews dedicated to agricultural robotic vehicles [12,27,[43][44][45] reflects the focus of the researchers on improving the intelligence of robots, but it also verifies that only a limited number of works address the energy performance assessment of the robotic platforms [46][47][48]. The existing scientific landscape indicates that, for the moment, researchers place more emphasis on immediate concerns and challenges, such as functionality and accuracy, rather than delving into the complexities of energy-related aspects, as agricultural robotics, especially in the context of electric vehicles, is still in its nascent stages.…”