关键词: 低空经济, 低空技术, 智慧果园, 无人农场, 无人机, 智慧农业

Abstract:

[Significance] Low altitude agricultural technology, with unmanned aerial vehicles (UAVs) as its primary platform, integrates 5G communication, artificial intelligence, and the Internet of Things to support data acquisition, analysis, and decision making throughout agricultural production. These advances are driving a transition from traditional experience based management toward a model in which data serve as the primary basis for decisions. As low altitude technology continues to advance in communication capacity, payload performance, and onboard processing, agricultural operations are undergoing profound changes. UAVs once used mainly for crop protection spraying have gradually evolved into multifunctional platforms capable of data collection, crop growth monitoring, image interpretation, precise input application, and operational assistance. Supported by a three dimensional integrated framework, which includes vertical integration, horizontal expansion, and spatio-temporal coordination, low altitude systems are reshaping field management structures, operational modes, and decision making processes. This transformation is accelerating the digitalization, networking, and intelligent upgrading of agriculture. This study aims to provide theoretical guidance and technical pathways for the broader application of low altitude technology in agriculture and to support the exploration of sustainable development models and industrial layouts for the low altitude agricultural economy. [Progress] The core contribution of low altitude technology to smart agriculture lies in establishing a complete sensing, decision, execution, and feedback cycle and implementing a four level structure comprising infrastructure, core technologies, application support, and scenario deployment. The infrastructure layer relies on 4G/5G networks, RTK high precision positioning, and ground based sensor systems. Multi source data are acquired through UAV mounted multi-spectral and thermal sensors working in coordination with ground monitoring devices to capture information on crop conditions and field environments. The core technology layer utilizes edge computing, cloud platforms, and analytical models to support growth assessment, pest and disease warnings, and other forms of analysis. At the application layer, UAVs operate in collaboration with ground equipment to implement precise crop protection, seeding, and irrigation, while also extending to field monitoring and agricultural logistics. This paper focuses on low altitude agricultural technology, summarizes its mechanisms and systematically reviewing the associated technical system from the perspectives of operational equipment, low altitude remote sensing and recognition, data processing and analysis, and precision operation and supervision. It further examines key functions enabling agricultural intelligence. Drawing on recent research and representative cases, the paper discusses practical applications in depth. In smart orchards, for example, the SCAU Smart Patrol system combined with the Lichi Jun model can deliver early pest and disease warnings two to three weeks before outbreak and support yield estimation. In ecological unmanned farms, integrated sky, air, and ground monitoring enables autonomous operation across plowing, planting, management, and harvesting. In production operations, agricultural UAVs have accumulated over 7.5 billion mu of service area globally, covering nearly one third of China's cultivated land area, saving approximately 210 million tons of water, and reducing carbon emissions by approximately 25.72 million tons. In logistics scenarios, transport assisted by UAVs in mountainous orchards improves efficiency more than tenfold while keeping damage rates below three percent. [Conclusions and Prospects] Sensors remain fundamental tools for capturing agricultural information and reflecting crop growth conditions. Developing highly generalizable technical modules helps lower application barriers and improve operational efficiency, while fusing multi scale data partially compensates for the limitations of single source information. Despite rapid progress, the low altitude agricultural economy still faces challenges including technological maturity, application cost, standardization, industrial integration, and workforce development. Based on an analysis of these challenges, this paper proposes building a three dimensional integrated technology framework featuring vertical integration, horizontal expansion, and spatio-temporal coordination; promoting the improvement and unification of technical standards; constructing an integrated industry ecosystem spanning research, manufacturing, application, and service; and strengthening policy support, industry norms, and talent training systems. These measures are expected to accelerate the emergence of new drivers of growth in the low altitude agricultural economy.

Key words: fruits and vegetables, cold chain monitoring, temporal multimodal fusion, dual attention mechanism, online non-destructive testing