ReviewSensors (Basel, Switzerland)2026
A Survey of Environmental Perception for Unmanned Ground Agricultural Machinery in Field Environments.
Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Unmanned ground agricultural machinery is required to operate efficiently in complex and dynamic field environments, which presupposes accurate and reliable environmental perception capabilities. This requires the machinery to perceive and respond to various typical elements in both driving and operational environments, such as obstacles, crop rows, and field boundaries. This paper focuses on typical environmental elements and analyzes the environmental perception technologies used in unmanned ground agricultural machinery during field navigation and operation. First, the working principles, advantages, limitations, and application scenarios of commonly used sensors, including vision and radar sensors, are comprehensively reviewed. In addition, the critical role of multi-sensor fusion in enhancing perception robustness and adaptability is highlighted. Subsequently, this paper centers on the specific environmental elements encountered by unmanned ground agricultural machinery. From this perspective, existing perception methods are systematically categorized and reviewed across three domains: image data, point cloud data, and multimodal data fusion. The performance differences and applicable scenarios of these methods in practical applications are also analyzed. Finally, the current challenges facing environmental perception technologies for unmanned agricultural machinery are analyzed, including multi-sensor fusion complexity, the computational-real-time trade-off, and the scarcity of specialized datasets. Future development trends and potential research directions are also discussed. This review aims to provide a reference and foundation for advancing environmental perception technologies in unmanned ground agricultural machinery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.