Artificial Intelligence and CEA Horticulture
As both a process and vocation, Controlled Environment Agriculture (CEA) is intimately entwined with technology. As CEA gets increasingly sophisticated through technological advancements, how these developments will affect horticultural processes, and in turn, world food supplies, remains largely unknown. At the forefront of new frontiers in Controlled Environment Agriculture technology lies Artificial Intelligence (AI). This technology can potentially alter the way in which humankind procures fresh produce, largely by replacing human labor with mechanized production.
We live in a world increasingly dependent upon, and defined by, technology. This notion rings true in almost every facet of day-to-day life in the twenty-first century. Similarly, for those living in the western world, this same technology contributes to meeting our general human needs. Case in point, food is readily available for most people at grocery stores within just a short drive. Yet, our current food subsistence patterns are far from infallible. Similarly, the cultivation technology that made commercial agriculture possible is falling short on several fronts. These shortcomings are evident in pesticide and preservative laden food sources. The same can be said about the hungry human populations in less-fortunate regions of the globe.
Over the last few decades, CEA production has grown leaps and bounds in its scale and capabilities. To this end, many feel that CEA practices such as urban agriculture and vertical farming will eventually help resolve global food crises. This positive outlook is largely attributable to technological advancements in “smart” environmental controls and horticultural lighting. This cultivation technology has helped make CEA production viable, as well as profitable, on a commercial scale for the first time. Along this line of thought, sophisticated irrigation systems have also drastically reduced the amount of human labor required in propagating crops. For many CEA advocates and technology authorities, AI is the next phase in streamlining and sophisticating horticulture procedure as well as global subsistence patterns.
Why Artificial Intelligence in Controlled Environment Agriculture Technology?
There are several reasons why AI has so much potential in the world of CEA. At this point, automation is already an essential element in most CEA growing, its benefits are related to both consistency and efficiency in operations. To illustrate, smart controllers take much of the guesswork out of troubleshooting environmental issues, while fertigation systems accomplish irrigation in a controlled and effective fashion. Nonetheless, AI has the capabilities to take these advancements even further.
Technology authorities postulate that AI can potentially circumvent human interaction with horticultural processes and garden maintenance almost in their entirety. To illustrate, according to the website Ag Funder News, “Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment…by using artificial intelligence and machine learning.” The important take away here is the idea that AI systems can actually learn, as well as make choices based upon, the objective constraints which dictate rational human decision making. Concerning both production and labor, this avant garde theory pushes notions of CEA automation to their absolute extremes. Because, this AI learning concept postulates that robots would actually be at the controls of environmental and irrigation systems that currently require human intervention in the way of fine tuning and decision making . In essence, this somewhat futuristic idea sets forth that AI could circumvent human error from these processes by removing the human labor needed to grow CEA crops.
While these ultramodern appraisals of AI and CEA cultivation can seem somewhat alarming, many see a real potential benefit in the technology. Because, humans are traditionally, well, undependable. This notion is heightened in more arduous, seasonal facets of CEA growing, where the industry is traditionally plagued by labor shortages in jobs that aren’t very attractive to even the most entry-level employees.
Many companies (in CEA and beyond) understand the struggles of finding dependable help for their businesses, with tech retail giant Amazon currently employing over 40,000 robots in its warehouse operations. In like fashion, there are several robots being developed that can handle more rudimentary CEA garden chores such as leaf pruning. When it comes to AI learning applications and higher-level jobs in CEA, it’s worth noting that even the most rudimentary human error in fertilization or irrigation can have devastating effects on crops. Large vertical farming companies such as Bowery are aware of this notion, and are currently implementing AI help in certain important facets of their operations. As such, Bowery has come to at least partially depend on the cutting-edge of AI technology as they integrate “machine learning” into their processes of environmental controls and plant care.
Cultivation Technology, Sensors, and Data Analytics
AI applications in CEA and beyond are entirely data driven. Within these systems, machines both gather and use information as the basis of machine learning. This sort of remote sensing, data compiling technology is already widely popular in more advanced CEA operations. This notion is especially ture regarding soil and air sensors. These controlled environment agriculture technology advancements are used to measure moisture levels in grow mediums, as well as temperature and humidity levels in the air. To this end, they give cultivators real-time updates on the working analytics of their operations.
Commercial agriculture operations are taking notions of remote sensors and data analytics to their extreme by way of drone sensing. To illustrate, some commercial farms use drones to fly over fields of crops and take precise readings of growth patterns and environmental conditions. This data informs agriculturists, in actual-time, what sort of elements their garden is lacking or burdened with. This inf allows them to make pro-active decisions in counteracting any potential problems. For the time being, drone technology in commercial agriculture is still being utilized to inform rational decision making in the human mind. However, many feel these processes will be integral with informing data matrices in the machine learning of AI technology.
Artificial Intelligence and Machine Learning
Potential advances, as well as drawbacks, for AI applications in CEA can be drawn from current mainstream research To again look to the Ag Funder News website, they report: “Interviews with some of the IBM project team members … revealed that the team believed it was entirely possible to ‘algorithm’ agriculture, meaning that algorithms could solve any problem in the world.” While IBM’s claims are doubtlessly grandiose and positivistic in nature, they are not entirely unreasonable. To illustrate, major computer tech companies, such as Google and Pandora, already utilize algorithms to isolate relevant data, make decisions, and finally cater to human needs. However, all these algorithmic applications depend upon objectively derived, predictable points of data in informing the machine learning process.
When it comes to creating algorithms for agriculture, researchers have been running into countless problems. These issues all stem from the unpredictability of the natural world. To illustrate, there are countless variables that contribute to weather patterns around the globe. Moreover, the nuances of these factors are far beyond the scope of human understanding. This notion is relevant because, humankind cannot program machines to learn about environmental systems that we don’t yet understand. Also, AI cannot make the critical step in accounting for anomalous factors in weather. As such, our technology is only as strong as the data that we supply it with via our own, human-derived knowledge.
Issues with environmental predictability that plague AI applications in traditional agriculture may not seem to effect CEA production as intensely. However, there are still factors which make AI fall short in controlling CEA growing in its entirety. For instance, many greenhouse gardens are still largely at the mercy of mother nature and present challenges with anomalous weather patterns. In a similar fashion, indoor gardening operations experience problems with such irregularities as equipment failure and power outages. Needless to say, at this point, only human intervention can rectify these issues.
Summary on Controlled Environment Agriculture Technology and AI
CEA crop production is only as strong as the equipment and human labor that supports it. The balance between these two variables continues to shift under the pressures of new advancements with technology such as AI. This notion presents an interesting crux in modern cultivation processes, as automation can provide a more efficient platform for consistency than seen with human labor. However, machines cannot account for the rhythms of the natural world or other operational variances. Along this line of thought, many accomplished horticulturists understand the “intuitive touch” it takes to produce exceptional crops. This intuition comes as a feeling rather than as a form of objective knowledge or data. All things considered, AI and other advancements in Controlled Environment Agriculture technology seem to have their place in today’s horticultural processes. However, this tech still cannot yet match the capacity for creativity explicit in the human mind.
This article originally appeared in the August / September 2019 edition of Maximum Yield Magazine.