Yépez-Ponce Darío Fernando, Salcedo José Vicente, Rosero-Montalvo Paúl D, Sanchis Javier
Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain.
Facultad de Ingeniería en Ciencias Aplicadas, Universidad Técnica del Norte, Ibarra, Ecuador.
Front Artif Intell. 2023 Aug 31;6:1213330. doi: 10.3389/frai.2023.1213330. eCollection 2023.
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.
近年来,随着耕地日益稀缺,精准农业和智能 farming 得到了跨越式发展。根据联合国粮食及农业组织(粮农组织)的数据,到2050年,全球农业产量应比目前水平增长约三分之一。因此,农民大量使用化肥来促进作物生长和提高产量,这对食品的营养改善产生了不利影响。为应对与生产力、环境影响、食品安全、作物损失和可持续性相关的挑战,农业中的移动机器人数量激增,主要集成了路径规划和作物信息收集过程。目前的农业机器人系统体积庞大且成本高昂,因为它们使用计算机作为服务器,移动机器人作为客户端。本文综述了移动机器人技术在农业中的应用,以降低成本、减少环境影响并优化收成。阐述了移动机器人技术的现状、所采用的技术、应用的算法以及在智能 farming 中取得的相关成果。最后,还介绍了新的智能 farming 技术面临的挑战:环境条件、实施成本、技术要求、过程自动化、连接性和处理能力。作为本文的贡献之一,可以得出结论,智能 farming 实施的领先技术如下:物联网(IoT)、移动机器人技术、人工智能、人工视觉、多目标控制和大数据。一种可以实施的技术解决方案是开发一种完全自主、低成本的农业移动机器人系统,该系统不依赖服务器。