The UX 2030 Series
As emerging technology becomes an increasingly ubiquitous part of our lives, the design decisions we make today will shape how these technologies impact the world over the decade to come.
This series envisions how we might apply emerging technology in specific industries to create positive impact. We’ll explore what might accelerate or hinder these realities and the key risk areas and unintended consequences to consider.
Illustration by Laura Carr
A version of this article was first published in IoT Now.
Access to healthy food is a staggering problem in the U.S. Some 19 million Americans live in food deserts, while up to 40% of food produced in the U.S. goes to waste. Moreover, the production, transportation, and distribution of food is the fifth-highest contributor to greenhouse gas emissions in the country. It’s clear that the existing food system faces an overwhelming efficiency problem.
Growing food is a reasonably well-understood science that humans have iterated on for thousands of years. Yet despite advancements in technology, agriculture is still one of the least digitized of all major industries, according to McKinsey. There is enormous opportunity to combine agricultural technology with the proliferation of the Internet of Things (IoT) to improve access to food in underserved communities.
We imagine a 2030 where IoT-enabled circular food production democratizes agricultural skills, improves efficiency, and can be personalized to meet community needs. These community solutions would augment – not replace – the existing agricultural system, providing supplementary access to healthy foods to those most in need.
So how do we get there – and what risks will we face along the way?
More accessible, efficient, and personalized food production
IoT has the unique ability to integrate and automate tasks that would require significant expertise or time, greatly improving efficiencies and offering novel ways to personalize experiences. As IoT evolves over the next decade, how might this technology help improve access to food?
While existing personal and community gardens have an important role to play in food access and urban development, they can be unrealistic to scale. The knowledge and work required to sow, tend, and harvest food at the right time and in the right way every day is a daunting task for anyone, especially those living in food deserts or underserved communities.
An automated IoT system could address this challenge by bringing specialized farming knowledge to laypeople. Imagine a communal rooftop garden on an apartment or commercial building where healthy produce can grow throughout the year. Yet rather than the people living or working in the building tending to the crops, the garden would be managed by a web of sensors, automated watering systems, and robotics for tasks such as sowing, pruning, and harvesting.
Specialized sensors could take on specific tasks of measuring watering levels, soil nutrition, as well as plant ripeness and health. With IoT sensors and fully connected system-on-a-chip (SoC) devices continuously becoming cheaper, a monitoring device could be deployed for nearly every plant on a rooftop garden. This means the time-consuming task of tending to plants can be carried out by inexpensive electronics rather than humans, reducing barriers to access and allowing more people to participate in, and reap the benefits of, urban farming.
Humans are increasingly developing novel and more sustainable ways to farm that involve less – or better managed – water, light, and soil. Combined with the possibilities of machine learning to identify the best time and manner to tend to and harvest plants, by 2030 we could establish robust farming operations in almost any location.
As systems of IoT-enabled devices and sensors work together in harmony to measure water and nutrient levels for each plant and communicate with connected pumps and other delivery systems, machine learning can aggregate these vast amounts of data and drive inputs which ensure ideal growth conditions. Rainwater collection systems, coupled with weather prediction models, could determine optimal watering schedules. Devices might direct the sun throughout the day to plants that need it most, or capture sunlight itself and store its energy for cloudy days.
An IoT-enabled system layer can manage the individual technologies used to grow food and organize which gardens might be best suited for which plants based on growing conditions inherent to its location and predictions for the needs of the people living in that community.
The connected and automated nature of IoT is well-placed to help determine a community’s real-time food needs and provide personalized distribution.
Just as the IoT system in aggregate could predict climate and resulting crop yields, it could also determine consumption patterns based on daily habits and anticipate the irregularities of a family and community’s schedules. Machine learning could detect patterns and anticipate food supply needs across a community, in order to allocate space to the produce in highest demand and efficiently distribute available produce within the community. A fully automated, communal garden could also be connected with other automated gardens, allowing for win-win sharing of crops and eliminating surplus that might otherwise go to waste.
Multiple communities could together make up a large system of interdependencies that can optimize the use of technology while distributing up-front costs across different investing areas. Even greater impact could come from partnering with existing local organizations such as food banks and community centers.
Addressing risk areas
Implementing such a complex, interconnected solution requires not only an understanding of human needs and technological constraints, but also the broader economic and social impact.
While the cost of IoT technology is continuously decreasing, the overall costs of establishing such a system are still significant. There unfortunately aren’t many examples of new technology adopted by underserved communities first – typically, those who can afford it create the economies of scale that make the technology accessible to a wider audience. Depending on population and density, a system such as this might not make financial sense for every food desert or underserved community – for example, distributing infrastructure costs may work for thousands of apartment dwellers but not for a hundred small-town inhabitants.
However, we have to look beyond the short-term investment costs and consider the long-term benefits of this system that other industries and stakeholders might find valuable. Start-ups like AeroFarms and Vertical Harvest are already leveraging technology to bring vertical farming to urban communities in the U.S., and governments are taking note as well: Singapore aims to triple domestic food production by 2030 through the use of technology-backed systems like multi-story urban LED farms and recirculating aquaculture systems. Industries from retail to healthcare could see a case for pursuing the positive long-term health outcomes of providing people with access to healthier food options.
Any system that relies on tremendous data collection in order to fuel machine learning models needs to be fortified against misuse of data and have a clear perspective on who retains control of it.
A highly interconnected system of IoT devices, robots, and machine learning models raises concerns about how privacy and user consent would be managed. Would people or communities be comfortable sharing their food consumption habits? Who else would have access to that information?
Privacy concerns may also be more significant for some communities than others. Lack of trust in government and centralized organizational bodies may be a barrier to adoption of a system that assumes people would be comfortable letting something as personal as food be handled by robots that are invisibly managed. Care must be taken to co-design such as system with members of the community, educate them on how it works, what data is collected, and how community members are empowered to control it.
Access to healthier foods alone does not ensure that people will use them. What we eat is a very personal decision with social, cultural, and educational impacts. How might systems like this change the relationships people and communities have with food? Could these systems support existing community organizations and resources that have a strong understanding of their communities’ unique needs? At the individual level, could they help people live and eat healthier?
Providing healthy produce is only one aspect of systemic change that helps people build new, sustainable eating habits. There will be a need for instruction and guidance in terms of nutrition, recipes, and motivation in order to encourage behavior change for those with busy schedules or no awareness or interest in adapting their lifestyle habits.
Designing with, not for
IoT represents a unique opportunity to solve some of the inefficiencies of food production and distribution, and with that, the ability to address inequities in food access.
Nevertheless, there are important challenges involved in creating an infrastructure that impacts such an important aspect of what we as humans need to survive. As designers, it’s critical to engage with communities of use when considering such systems, elevating their needs and lived experiences, and ensuring that we design with, not for, them. Moreover, we need to approach such problems with a systems-thinking mindset that considers all people and groups potentially affected by the change, whether they ever come in direct contact with it or not.
It’s a difficult challenge, but an imperative to avoid unforeseen consequences and design for preferable outcomes. In leveraging this responsible design approach, we might imagine a future where IoT is used not only to bring healthy food closer to underserved areas, but bring people closer to each other, as a community.