Applying cutting-edge technology to revolutionize our approach to food cultivation comes with significant advantages, but is the investment truly justified?
When a robotics expert and a sixth-generation farmer join forces to establish a company, their primary focus revolves around a pressing issue: climate change.
In 2020, Gilwoo Lee, the robotics specialist, and Casey Call, the seasoned farmer, co-founded Zordi, an innovative agricultural platform that integrates AI and robotics with greenhouse farming. Lee, a recent graduate from the University of Washington, was deeply impacted by the wildfires that besieged his surroundings, serving as a stark reminder of the reality of climate change. “That was just a very strong signal of climate change happening. I was already committed to starting my own company with something where my robotics and AI can make a big difference when it comes to impact,” Lee emphasized.
Call, who assumes the role of head grower and agronomist at Zordi, has witnessed firsthand the influence of sustainability on his family’s extensive farmland spanning 12,000 acres in western New York, where they cultivate a variety of crops. “My whole life, it’s been overwhelmingly convincing that agriculture needs to get more efficient,” Call stressed.
Zordi, a startup in agriculture supported by Khosla Ventures, recently emerged from stealth mode, harnessing robotics, AI, and traditional farming expertise to cultivate strawberries in greenhouses in the Northeast. With human oversight, robots handle tasks ranging from planting to harvesting a unique strain of strawberries imported from Japan and Korea. Through the utilization of AI and machine learning, Zordi monitors the growth process and regulates the greenhouse environment, with robots also responsible for harvesting the ripe produce.
Quality strawberries pose a complex growing challenge, making the market particularly lucrative, as noted by Lee. The choice of strawberries was deliberate due to their specific climate requirements and delicate nature during harvesting. Lee envisions expanding their success with strawberries to other crops once they establish a reliable delivery system to stores. “If we’re able to do this and actually get them successfully delivered to the stores, then we’re pretty confident that you can extend the harvesting tools to other crops,” Lee expressed. “Controlled environment agriculture or greenhouses, for us, is a very good way to feed the world with sustainably grown local fresh produce, and that was the mission that I wanted to see happen,” she added.
While the association between artificial intelligence, machine learning, and sustainable agriculture may not be immediately apparent, the industry is witnessing a surge in advanced technology adoption to comprehend vast amounts of data encompassing microclimates and soil pH levels.
Vonnie Estes, the vice president of innovation at the International Fresh Produce Association (IFPA), highlights the plethora of information available to farmers through dashboards, satellites, weather reports, and sensors, emphasizing the need for standardization in the agricultural sector. The advent of AI is poised to revolutionize decision-making processes, enabling farmers to optimize resource usage such as water and pesticides judiciously.
The integration of artificial intelligence and machine learning in agriculture traces back to the emergence of computer technology in the 1960s, leading to the evolution of precision farming in the late ‘80s and early ‘90s. This approach aimed to enhance crop optimization at a field level, introducing tools like GPS and field monitoring systems. Subsequent advancements in data capture technologies, particularly with the introduction of agricultural drones (UAVs) in the 2010s, facilitated real-time data collection on crops and livestock. The convergence of cloud computing, big data, machine learning, and AI has empowered farmers with predictive analytics for diverse aspects such as crop yields, disease detection, and optimal planting and harvesting schedules.
Ilias Tagkopoulos, director of the AI Institute for Food Systems (AIFS) at the University of California, Davis, underscores the integration of AI technologies in agricultural practices, ranging from drones for crop monitoring to automated pesticide application, emphasizing the pivotal role of AI in enhancing agricultural production efficiency.
Despite agriculture’s substantial contribution to greenhouse gas emissions, advancements in AI and ML present an opportunity to mitigate the sector’s environmental impact. As climate change jeopardizes crop yields, farmers are increasingly turning to AI and ML to foster sustainable practices and climate-resilient strategies.
With the global population projected to exceed 9 billion by 2050, the demand for food production is set to surge dramatically. Ranveer Chandra, managing director of industry research and CTO of agri-food at Microsoft, underscores the pivotal role of AI and ML in meeting the escalating food requirements sustainably. By leveraging data-driven insights, farmers can enhance decision-making processes, moving away from traditional guesswork towards informed agricultural practices.
Innovations like John Deere’s “See & Spray” technology exemplify the transformative potential of AI in agriculture, enabling precise application of resources at the individual plant level. This targeted approach not only optimizes resource utilization but also minimizes environmental impact by reducing overspraying and wastage.
Addressing the issue of food waste, AI technologies offer solutions to optimize harvesting schedules based on precise parameters like sugar content, minimizing wastage due to overripeness or crop damage. Such data-driven approaches empower farmers to make informed decisions, thereby enhancing operational efficiency and sustainability.
While the adoption of AI and ML presents lucrative opportunities for large corporations, it also holds promise for smaller organic farmers. Smallhold, an organic mushroom farming enterprise, exemplifies how AI-driven systems can optimize growing conditions by monitoring and adjusting environmental parameters in real time. By integrating data analytics and specialized algorithms, Smallhold ensures optimal mushroom growth while minimizing resource consumption.
Despite the optimistic outlook on AI’s role in sustainable farming, concerns regarding potential drawbacks and risks linger. The reliance on AI without human supervision raises uncertainties about unexpected outcomes, prompting the need for vigilant oversight to avert undesirable consequences. Security threats pose a significant challenge, necessitating robust measures to safeguard farm operations against potential cyber risks.
Moreover, the transition towards AI-driven agriculture may have implications for labor dynamics, potentially transforming traditional farm work into supervisory roles overseeing automated processes. This shift could exacerbate existing inequalities and contribute to a widening gap between skilled and unskilled labor in the agricultural sector.
Connectivity issues also emerge as a critical consideration, with rural areas requiring robust broadband infrastructure to leverage AI and ML technologies effectively. Addressing the digital disparity between rural and urban communities is essential to ensure equitable access to technological advancements in agriculture.
Data ownership and accessibility present another frontier, with larger corporations poised to benefit disproportionately from AI implementation due to their capacity to monetize data insights. While the cost of deploying AI tools remains a barrier for many farmers, the broader agricultural community stands to benefit from the industry-wide advancements catalyzed by AI and ML innovations.
In conclusion, as agriculture confronts mounting challenges from climate change and resource constraints, AI and ML emerge as indispensable tools for farmers to navigate these complexities. The collaborative efforts of industry leaders, tech innovators, and grassroots farmers underscore the transformative potential of AI in steering agriculture towards a sustainable, efficient, and climate-resilient future.
