Global Navigation Satellite Systems as State-of-the-Art Solutions in Precision Agriculture: A Review of Studies Indexed in the Web of Science

• Published in: Agriculture (Basel) Vol. 13; no. 7; p. 1417
• Main Authors: Radočaj, Dorijan, Plaščak, Ivan, Jurišić, Mladen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Accuracy; Agricultural industry; Agricultural production; Agriculture; Algorithms; Artificial satellites; Citation indexes; citation topics micro; computers; Cost control; Deep learning; Geospatial data; Geostatistics; Global navigation satellite system; Global positioning systems; GNSS; GPS; Harvesting; Internet; Internet of Things; Lidar; Literature reviews; Localization; Mapping; multi-constellation receivers; precise point positioning; Precision agriculture; Queries; R&D; Rankings; Remote sensing; Research & development; Robots; Satellites; Science; Signal processing; Simultaneous discrimination learning; Simultaneous localization and mapping; Software; Spatial data; State of the art; Unmanned aerial vehicles; United States > US; China
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture13071417

Global Navigation Satellite Systems (GNSS) in precision agriculture (PA) represent a cornerstone for field mapping, machinery guidance, and variable rate technology. However, recent improvements in GNSS components (GPS, GLONASS, Galileo, and BeiDou) and novel remote sensing and computer processing-based solutions in PA have not been comprehensively analyzed in scientific reviews. Therefore, this study aims to explore novelties in GNSS components with an interest in PA based on the analysis of scientific papers indexed in the Web of Science Core Collection (WoSCC). The novel solutions in PA using GNSS were determined and ranked based on the citation topic micro criteria in the WoSCC. The most represented citation topics micro based on remote sensing were “NDVI”, “LiDAR”, “Harvesting robot”, and “Unmanned aerial vehicles” while the computer processing-based novelties included “Geostatistics”, “Precise point positioning”, “Simultaneous localization and mapping”, “Internet of things”, and “Deep learning”. Precise point positioning, simultaneous localization and mapping, and geostatistics were the topics that most directly relied on GNSS in 93.6%, 60.0%, and 44.7% of the studies indexed in the WoSCC, respectively. Meanwhile, harvesting robot research has grown rapidly in the past few years and includes several state-of-the-art sensors, which can be expected to improve further in the near future.