Search and you'll find countless articles touting the endless potential of artificial intelligence and other emergent technologies to transform all of industry, agriculture included. But farmers on the fence about AI's ROI want specifics before sinking hard-earned dollars into AI-driven infrastructure — it's understandable. So what, exactly, are the benefits that machine learning, machine vision, connected sensors, and other AI tools have already delivered to AI-fitted farms, and where is AI's influence likely to head in the future?
These industry insiders shared their perspectives on the topic. Here's what they had to say:
1. Ash Madgavkar, Founder of Ceres Imaging
“Data is leading the way to truly sustainable farming solutions. By sustainable, I mean both more efficient use of resources as well as more control over your bottom line. Again and again, growers are using our tools to cope with the many things outside their control, such as severe weather events and trade wars. Tools powered by AI can help give growers solutions to building long-term, sustainable businesses.”
2. John McDonald, CEO at ClearObject
“Intelligence itself is the most prominent benefit for agricultural operations worldwide. And it comes in various forms. Back to precision farming and its associated smart technologies, for instance, satellites can scan farm fields and identify crops that need more water, fertilizers, and so on. Images from satellites also provide data to help farmers make more informed decisions, such as the best places to plant to avoid pests. Similarly, a smart attachment on a tractor can determine different treatments for crops depending on their health, and can even use big data to differentiate between plants and weeds for intelligence-based pesticide control.”
3. Kirk Haney, CEO of Radicle Growth
“Farming is all about managing and reducing risk. AI can look at all the disparate variables and help a farmer make the best, risk-adjusted decision. This is where it will have its biggest impact.”
4. John Corbett, CEO of aWhere
” Insight-driven/supported decisions. The warming atmosphere and increasingly variable weather is driving tremendous uncertainty and increased risk in agriculture. Leveraging analytical assessments, even environmental trend analysis, must guide investment to dampen the impact of variability. AI and machine learning identifies patterns and will support solutions that literally will shift what crop to grow where and when to plant.”
5. Jeff Klaumann, Chief Technology Officer at Internet of Things America
“AI and machine learning-enabled solutions increase yields and productivity to intersect the considerably rising global food production demands. The world needs more food. The United Nations projects that the global population will increase from $7.8 billion in 2015 to $9.7 billion in 2050. While there is debate over whether the population growth doubles world food production demand, agribusiness and scholars alike agree that global food production demand will significantly increase by at least 70%. This matches historical trends for global food production, where agricultural production has more than tripled since 1960. Agriculture and food systems around the world have evolved significantly during this period and will need to continue to adjust further to meet global food production demands.
Agriculture in the U.S. has been focused on exporting crops and resource-intensive meat and dairy products. Meeting the needs of current food production demands across the globe requires utilizing productivity strategies and innovative technologies. AI and machine learning is the critical component for producers to maximize crop yields and operational efficiencies at the scale needed to address food production demands worldwide.”
6. Brad Constantinescu, President and CTO of Stone Soup Tech
“AI is all about automating and optimizing. It aims to generate the highest possible production per unit of land and the lowest possible price per end product. It aims to achieve these by:
– Lowering wage costs – Wages will grow, as farmers will need to use more advanced tools, but fewer people will be employed
– Lowering maintenance costs and losses due to failures (by relying on predictive maintenance)
– Lowering losses due to diseases or pests
– Optimizing the use of resources by relying on advanced data, predictive analytics, hyperlocal weather forecasts.”
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