Digital agriculture, with the incorporation of Internet-of-Things (IoT)-based technologies, presents the ability to control a system at multiple levels (individual, local, regional, and global) and generates tools that allow for improved decision making and higher productivity. Recent advances in IoT hardware, e.g., networks of heterogeneous embedded devices, and software, e.g., lightweight computer vision algorithms and cloud optimization solutions, make it possible to efficiently process data from diverse sources in a connected (smart) farm. By interconnecting these IoT devices, often across large geographical distances, it is possible to collect data at different time scales, including in near real-time (i.e., with delays of only a few tens of seconds). This data can then be used for actionable insights, e.g., precise applications of soil supplements and reduced environmental footprint. Through LATTICE, we present an integrated vision for IoT solutions, data processing, and actionable analytics for digital agriculture. We couple this with discussion of economics and policy considerations that will underlie adoption of such IoT and ML technologies. Our paper starts off with the types of datasets in typical field operations, followed by the lifecycle for the data and storage, cloud and edge analytics, and fast information-retrieval solutions. We discuss what algorithms are proving to be most impactful in this space, e.g., approximate data analytics and on-device/in-network processing. We conclude by discussing analytics for alternative agriculture for generation of biofuels and policy challenges in the implementation of digital agriculture in the wild.

中文翻译：

---

格子：大规模数字农业的机器学习、数据工程和政策考虑的愿景

数字农业结合了基于物联网 (IoT) 的技术，提供了在多个层面（个人、本地、区域和全球）控制系统的能力，并生成了允许改进决策和更高水平的工具生产率。物联网硬件（例如异构嵌入式设备网络）和软件（例如轻量级计算机视觉算法和云优化解决方案）的最新进展使得在互联（智能）农场中高效处理来自不同来源的数据成为可能。通过互连这些物联网设备，通常跨越很长的地理距离，可以收集不同时间尺度的数据，包括近实时（即只有几十秒的延迟）。然后可以将这些数据用于可操作的见解，例如，土壤补充剂的精确应用并减少环境足迹。通过莱迪思，我们为数字农业的物联网解决方案、数据处理和可操作的分析提出了一个综合愿景。我们将此与经济和政策考虑因素的讨论结合起来，这将成为采用此类物联网和机器学习技术的基础。我们的论文从典型现场操作中的数据集类型开始，然后是数据和存储、云和边缘分析以及快速信息检索解决方案的生命周期。我们讨论了在这个领域中哪些算法被证明是最有影响力的，例如，近似数据分析和设备上/网络内处理。我们最后讨论了替代农业的分析，以生成生物燃料以及在野外实施数字农业时面临的政策挑战。