AI Use Cases in Agriculture
Tim Hammerich
News Reporter
Ag technology providers have been promising more efficient ways to various sources of farm data into actionable decisions and more profitable operations. Can AI turn those promises into realities? Jim Ethington, CEO of Arable says thatbelieves by combining data imagery, voice capabilities, and forecasting, agronomists can unlock new capabilities.
Ethington... "With what's happening so rapidly in the language model front, but it's not just language models and chatbots following what's happening with images, what's happening with voice, what's happening with generative models and what that can do for weather forecasts. Like I think there's going to be so many opportunities in agriculture to apply those technologies and not in some sort of vague buzzword way, but in ways that deliver real, real value. We do a lot with images because we produce them all the time. And what we can now do without having to go spend two years generating training data, but to just actually feed images into a model and get segmentation back or get identification back, you know, it's light years ahead of where it was a couple of years ago. And I don't think it's slowing down. I think we're going to see it infuse. All of these different use cases, you know, from forecasting and prediction to image recognition, to definitely the language models and sort of the language-based, like almost chat experience as well, where I think there's a huge opportunity to augment agronomists around the world with that technology if they are provided with and trained on the right data."
Ethington added that we’re still a ways from that, but the techology is progressing rapidly.
