Lychee is an important cash crop in southern China. The excessive growth of winter shoots in the early winter season will lead to an increase in nutrient consumption, which in turn affects flower bud differentiation and fruit yield. To address the issue of low efficiency in traditional manual measurement methods, this study proposes an automated detection method using UAV remote sensing technology and an improved YOLOv8n_OBB_SEB algorithm. Through multi-dimensional optimization, this method successfully solves the issue of the small size of winter shoots, similar color to branches, and leaf occlusion in the orchard environment. The specific improvements include: using the SAHI algorithm for image slicing to assist inference to improve the recognition ability of small targets; embedding the Starblock in the StarNet model into the C2f module and replacing the original C2f module in the Backbone, which reduces the number of parameters and strengthens the feature extraction ability; replacing the Concat module in the Neck part with the BiFPN structure to optimize multi-scale feature fusion; introducing the EMA attention mechanism and embedding it into the C2f module in the Neck part to achieve pixel-level attention allocation and enhance the distinguishability between the target and the background. The experimental results show that on the lychee winter shoot test set, the detection accuracy of the improved YOLOv8_OBB_SEB algorithm reaches 89.2%, which is 20.7% higher than that of the original YOLOv8_OBB algorithm. Compared with other mainstream algorithms, YOLOv8_OBB_SEB shows stronger competitiveness and robustness. Through inference detection, the four coordinates of the target rotation box can be obtained, and the actual size can be calculated by converting the pixel height to estimate the real length of the lychee winter shoots. According to the estimation results, this paper divides the winter shoots into two groups: those requiring drug intervention and those not requiring drug intervention. The specific judgment standard is that when the length of the winter shoot exceeds 3 centimeters, it is classified into the group requiring drug intervention, and when the length of the winter shoot is less than 3 centimeters, it is classified into the group not requiring drug intervention. Remote sensing data of 24 lychee trees were collected on December 3, 2024. The spraying requirements were determined through manual field surveys, which were then compared and verified with the model inference results. Finally, it was concluded that the accuracy of the model reached 83.3%. This classification method provides reliable decision support and a clear decision-making basis for the precise management of winter shoots.
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Agricultural 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. The aim is 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.
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 agricultural low altitude agricultural technology, summarizes its mechanisms and systematically reviews 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 intelli-gence. Drawing on recent research and representative cases, the paper discusses practical applications in depth. In smart orchards, for example, the South China Agricultural University 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 (500 million hectare) times 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.
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.
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