<sc>Lattice</sc>: A Vision for Machine Learning, Data Engineering, and Policy Considerations for Digital Agriculture at Scale

Abstract

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.

Keywords

• Data integration
• data analysis
• internet of things
• Sensor systems
• cloud computing