发表机构
Missouri University of Science and Technology（密苏里科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无人机辅助智慧农业中能量受限无法收集所有传感器数据的问题，提出效用驱动的空间采样框架，按科学效用评分选择高价值网格单元，实现自适应科学数据管理。
AI 中文摘要
大型智慧农业部署从空间分布的传感器中持续产生科学数据，包括土壤、湿度、温度、作物健康以及害虫相关的测量数据。然而，在广阔的农业领域中，能量受限的无人机（UAV）往往无法在每次任务中从每个传感器收集数据。现有的无人机辅助收集方法通常优化覆盖范围、路线长度、数据量或新鲜度，但它们并不总是区分仅仅是可用的数据与具有科学价值的数据。本海报介绍了一种用于无人机辅助智慧农业的效用驱动的空间采样框架。该领域被划分为网格单元，每个单元的大小根据无人机地面覆盖范围来确定。在初始探索阶段之后，每个单元根据新鲜度、冗余度、异常可能性以及模型不确定性获得一个科学效用评分。然后，无人机在电池和返回基地的约束下，选择并访问一部分高效用单元。所提出的框架将基于无人机的收集重新定义为自适应的科学数据管理，而非穷举式感知。
英文摘要
Large smart-farming deployments generate continuous scientific data from spatially distributed sensors, including soil, humidity, temperature, crop-health, and pest-related measurements. In vast agricultural fields, however, an energy-constrained unmanned aerial vehicle (UAV) often cannot collect data from every sensor during each mission. Existing UAV-assisted collection methods typically optimize coverage, route length, data volume, or freshness, but they do not always distinguish between data that is merely available and data that is scientifically valuable. This poster introduces a utility-driven spatial sampling framework for UAV-assisted smart farming. The field is partitioned into grid cells, each sized according to the UAV ground coverage range. After an initial exploration phase, each cell receives a scientific utility score based on freshness, redundancy, anomaly likelihood, and model uncertainty. The UAV then selects and visits a subset of high-utility cells under battery and return-to-base constraints. The proposed framework reframes UAV-based collection as adaptive scientific data management rather than exhaustive sensing.
Comments2 pages, 1 figure. Accepted and presented as a poster at the 38th International Conference on Scalable Scientific Data Management (SSDBM 2026), San Diego, California, August 12-13, 2026
