Deep learning research is harvesting better ways to manage farms.

What’s new: A convolutional neural network predicted corn yields in fields across the U.S. Midwest.

How it works: Researchers from the University of Illinois at Urbana-Champaign built a network that forecasts the quantity of corn that will grow seasonally in a given field under variable rates of seeding and nitrogen fertilization.

  • The researchers chose nine experimental fields in Illinois, Ohio, Nebraska, and Kansas, with an average size of nearly 100 acres.
  • Their best-performing model subdivided each field into plots 5 meters square. For each square, the researchers entered various levels of seed and fertilizer along with elevation, soil quality, and satellite imagery.
  • The five inputs were fed into separate convolutional layers as raster images representing parameters of each square. These layers had access to only one parameter each and were not combined until the final fully connected layers.
  • Keeping the inputs in separate layers until late in the network helped the architecture process spatial data that varied significantly over space; for instance, soil quality or elevation that differed from one square to the next.

Results: The team’s model averaged .70 root mean squared error of the mean yield standard deviation in all fields. It predicted yields more accurately than other neural networks the team built in all but one. It was also better than a set of non-neural benchmarks, outperforming a random forest model by 29 percent and a multiple linear regression model by 68 percent.

Behind the news: Agriculture requires farmers to manage numerous environmental factors and decision points, from weather patterns to hiring manual labor. Machine learning can help at every stage. Big-ag heavyweights like John Deere as well as startups like Dot and SwarmFarm offer highly automated tractors including machines that use advanced image recognition to kill individual weeds. Landing AI helped design a rig that automatically optimizes harvesting. (Disclosure: Andrew Ng is CEO of Landing AI.) Other companies specialize in evaluating produce quality, crop health, and multi-farm operations.

Why it matters: Systems like this could help farmers increase yields, save on seed costs, and reduce excess nitrogen that ends up running off into water sources. The authors are performing more trials to improve the model and working on an optimization algorithm so farmers can generate fertilizer and seed maps for their own fields.

We’re thinking: In many developing economies, younger people don’t want to make their living from farming, and small family-run farms are being consolidated into larger plots. This creates opportunities for AI and automation to make agriculture more efficient, and potentially to make food more affordable and protect the environment.


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