Many of the top names in tech — IBM and John Deere among them — have invested heavily in AI implementation for agricultural ends. You can bet that these investments aren't going to be wasted. Whether it's small farms or industrial-scale ag operations, the potential of AI technology in farming is going to be tested. So, where are we in this process? How is AI impacting the state of agriculture right now? And what role will it play in agriculture's future?
These industry insiders stopped by to give us their insights on that question. Here's what they had to say:
1. Ash Madgavkar, founder of Ceres Imaging
“There are three big areas where AI is playing a more significant role in agriculture. Those include predictive analytics, crop and soil management, and robotics. They all contribute to helping farmers find solutions for a variety of issues, from water stress to disease to weed control to weather adjustments. That’s why AI is seeing rapid adoption in agriculture and its role in developing and providing predictive data is ultimately contributing to increases in crop yields, crop quality and crop sustainability.”
2. John McDonald, CEO at ClearObject
“AI in agriculture continues to unlock the power of precision farming, a model that was first introduced a few years ago. The goal of this movement remains to raise the crop yield as much as possible while investing as little as possible. With artificial intelligence as the overarching driver, precision farming is increasingly being adopted with the help of technologies such as satellites and smart tractors (see response #4). AI is also using big data to a larger degree given that more data is continually being collected and analyzed for agricultural purposes globally.”
3. Kirk Haney, CEO of Radicle Growth
“AI is aggressively coming after ag, but with an undigitized, unconnected supply chain, we have a solution looking for a problem. AI’s transformation of ag will happen, but it is going to take a few more years to revolutionize compared to the disruption that has occurred in other industries. McKinsey stated in 2016 that ag is the least digitized industry in the world, so we need to mindful of the need to get ag connected so AI can do its thing! Full Harvest is an example of one of our portfolio companies digitizing the supply chain with their B2B digital marketplace.”
4. Jeff Klaumann, Chief Technology Officer at Internet of Things America
“Producers increasingly look toward technologies and innovations to help them grow food more sustainably. For example, precision agriculture is quickly evolving to predictive agriculture with the adoption of a data-driven agronomy, encompassing imagery, sensors and artificial intelligence platforms. However, limited connectivity options in rural America continue to hinder adoption of powerful technologies and innovations in AI. Still, precision agriculture is experiencing significant growth worldwide due to AI, with Europe leading in market share. The United States will gain a larger market share position over the next five years as connectivity options gradually improve across rural and underserviced areas.”
5. Roger Royse, founder of the Royse AgTech Innovation Network
“AI is in development across a wide set of use cases to reduce labor and labor costs. AI combined with imaging has long been able to sort fruit in the warehouse and on the assembly line. Combined with robotics, learning systems can be used to identify and eliminate invasive species of plants, such as weeds. Many companies are trying to develop AI systems that can pick and sort fruit in the field, and there have been promising developments so far.”
6. Brad Constantinescu, President and CTO of Stone Soup Tech
“There are many ways AI is currently used in the agriculture industry:
– automated machinery: self-driving tractors (such as those developed by John Deere in partnership with Taranis) cut down on human costs, are more efficient and can work around the clock
– predictive maintenance: IoT sensors are used to monitor equipment (companies such as FarmersEdge), detect early signs of wear and avoid failures, reducing maintenance costs and improving production
– predicting diseases or pests: IoT sensors and aerial imagery (companies such as Agrii) are used to detect early signs of diseases, pests or other problems affecting livestock or crops; addressing them as fast as possible reduces the need for harmful and expensive chemicals, while also increasing production
– more advanced (hyperlocal) weather models (companies such as InitWeather): better weather data helps farmers plan when and how to deploy their resources”
7. Stephen Blum, CTO of PubNub
“Agricultural predictions is arguably the biggest impact machine learning will make on agriculture – taking massive sets of data, both structured and unstructured, and predicting future outcomes based on it. How will certain crops grow in certain climates based on past performance? What specific traits of plants perform better at what times in what soil types?
Agriculture companies can run any number of simulations, utilizing data not only from their own yields but shared data from other yields as well. Being able to predict the performance of crops sets farms and factories up for higher chances of success, and better, more sustainable yields.”
8. John Corbett, CEO of aWhere
“In many ways, entrepreneurs are far ahead of mainstream information-driven agriculture. The state of AI in ag might be thought of as having a dumbbell shape: LOTS of attention to elite analytics (entrepreneurs to multiple international companies) but the actual utilization of these analytics is deceptively low. Traditional farmer behavior has not actually changed all that much, though clearly the popular press has a growing portfolio of stories and quotes from farms that failed miserably due to some ‘unusual weather event’ – the point being, the data and information-rich potential is huge, yet for most of the world’s farmers (small-holder, tropical), the benefits of AI in ag have yet to be realized.”
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