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The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity stemming from the challenge of integrating diverse, often non-interoperable hardware and software components. In order to tackle these complexities to increase farm efficiency and understand the tradeoffs of these new DA innovations we developed Realtime Optimization and Management System (ROAM), which is a decision-support system developed to find a Pareto optimal architectural design to build DA systems. To find the Pareto optimal solution, we employed the Rhodium Multi-Objective Evolutionary Algorithm (MOEA), which systematically evaluates the trade-offs in DA system designs. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined objectives and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a DA architecture design method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability. 

This paper explores how conceptions of societal impact are produced and performed during academic computer science research, by leveraging critical technical practice while building a digital agriculture networking platform. Our findings reveal how everyday practices of envisioning and building infrastructure require working across disciplinary and institutional seams, leading us as computer scientists to continuously reconceptualize the intended societal impact. By self-reflectively analyzing how we accrue resources for projects, produce research systems, write about them, and maintain alignments with stakeholders, we demonstrate that this seam work produces shifting simulacra of societal impact around which the system’s success is narrated. HCI researchers frequently suggest that technical systems’ impact could be improved by motivating computer scientists to consider impact in system-building. Our findings show that institutional and disciplinary structures significantly shape how computer scientists can enact societal impact in their work. This work suggests opportunities for structural interventions to shape the impact of computing systems. 

Edge computing is a distributed computing paradigm that moves data-intensive applications and services (e.g., AI) closer to the data source. The rapid growth of edge endpoints connected to the Internet today poses several challenges in scalable application life cycle management. That is, managing data and workloads on several thousand, up to millions of edge endpoints, challenged by limited connectivity, resource constraints, network and edge endpoint failures. In this work, we present EdgeRDV, a new edge abstraction that builds on the idea of rendezvous nodes to manage edge workloads at scale. The EdgeRDV architecture is comprised of a central cloud management endpoint (or cloud hub), a central gateway for each edge site (or edge hub), redundant gateways (or rendezvous nodes), and edge endpoints. Beyond its scalable architecture, EdgeRDV presents new techniques and algorithms that address single points of failures and provide adjustable levels of resilience and cost-effectiveness in edge network deployments. We conducted preliminary experiments to evaluate EdgeRDV, through simulations, and our results show that EdgeRDV requires one to three orders of magnitude fewer intermediate nodes compared to relay structures, can gracefully adapt to failures, and requires a constant number of messages during failure recovery in edge sites with up to 667K+ edge endpoints. 

This work is an experience with a deployed networked system for digital agriculture (or DA). Digital agriculture is the use of data-driven techniques towards a sustainable increase in farm productivity and efficiency. DA systems are expected to be overlaid on existing rural infrastructures, which are known to be less robust. While existing DA approaches partially address several infrastructure issues, challenges related to data aggregation, data analytics, and fault tolerance remain open. In this work, we present the design of Comosum, an extensible, reconfigurable, and fault-tolerant architecture of hardware, software, and distributed cloud abstractions to sense, analyze, and actuate on different farm types. FarmBIOS is an implementation of the Comosum architecture. We analyze FarmBIOS by leveraging various applications, deployment experiences, and network differences between urban and rural farms. This includes, for instance, an edge analytics application achieving 86% accuracy in vineyard disease detection. An eighteen-month deployment of FarmBIOS highlights Comosum’s fault tolerance. It was fault tolerant to intermittent network outages that lasted for several days during many periods of the deployment. We introduce active digital twins to cope with the unreliability of the underlying base systems. 

The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity. To increase farm efficiency and understand the tradeoffs of these new DA innovations we developed ROAM, which is a decision support system developed to find a Pareto optimal architectural design to build DA systems. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined metrics and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and to visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability. 

Rural infrastructure is known to be more prone to breakdown than urban infrastructure. This paper explores how the fragility of rural infrastructure is reproduced through the process of engineering design. Building on values in design, we examine how eventual use is anticipated by engineering researchers building on emerging infrastructure for digital agriculture (DA). Our approach combines critically reflective technical systems-building with interviews with other practitioners to understand and address moments early in the design process where the eventual effects of DA systems may be being built-in. Our findings contrast researchers’ visions of seamless farming technologies with the seamful realities of their work to produce them. We trace how, when anticipating future use, the seams that researchers themselves experience disappear, other seams are hidden from view by institutional support, and seams end users may face are too distant to be in sight. We develop suggestions for the design of these technologies grounded in a more artful management of seamfulness and seamlessness during the process of design and development.