The Reality of Artificial Intelligence

The following article is based on Scott Shearer's presentation at the 2019 Precision Farming Dealer Summit. To watch the presentation, click here.

Right now, agriculture is in the hands of multiple individuals, making it disorganized. It has not been rolled up under big corporations and there are still a lot of independent business people in some respects. What is happening now with ag tech startups is there are tremendous amounts of venture capital going into ag technology as more people realize that data is going to start to consolidate agriculture.

There are a number of technological advancements coming up that are going to change the agriculture business. I used to talk about the future being 15-20 years out. Then I started to talk about it being 5-10 years out. Now I’m talking about it being a few years out. These technological advancements are already out there, working in another industry or they are being developed right now.

Neural Networks

At Ohio State, my PhD student Chris Weigman has been working on the latest in artificial intelligence, particularly in neural network classifiers. Neural networks are essentially a computer representation of what is going on in the human brain. What Chris is working on is training these neural network classifiers to recognize issues in crops like nitrogen deficiency.

 

Technology to Keep an Eye On

In this evolving technological world, there are a number of advancements worth watching in the agriculture world:

  • Amazon Web Services (AWS) is building cloud storage facilities and…
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Scott Shearer

Scott Shearer, is professor and chair of the Food, Agricultural and Biological Engineering department at Ohio State University. He specializes in precision farming, including autonomy, guidance systems, GPS and equipment automation. During his 30 years in academia, Shearer’s research efforts have focused on spectral and spatial image processing for the extraction of features for classifying agricultural settings; and controls and methodologies for metering and distribution of inputs in grain crop production systems.

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