Using AI deep learning to sort cucumbers
Kaz Sato has a very interesting report of using Google’s open-source machine-learning library TensorFlow:
It’s not hyperbole to say that use cases for machine learning and deep learning are only limited by our imaginations. About one year ago, a former embedded systems designer from the Japanese automobile industry named Makoto Koike started helping out at his parents’ cucumber farm, and was amazed by the amount of work it takes to sort cucumbers by size, shape, color and other attributes.
Makoto’s father is very proud of his thorny cucumber, for instance, having dedicated his life to delivering fresh and crispy cucumbers, with many prickles still on them. Straight and thick cucumbers with a vivid color and lots of prickles are considered premium grade and command much higher prices on the market.
But Makoto learned very quickly that sorting cucumbers is as hard and tricky as actually growing them. “Each cucumber has different color, shape, quality and freshness,” Makoto says.
In Japan, each farm has its own classification standard and there’s no industry standard. At Makoto’s farm, they sort them into nine different classes, and his mother sorts them all herself — spending up to eight hours per day at peak harvesting times.
“The sorting work is not an easy task to learn. You have to look at not only the size and thickness, but also the color, texture, small scratches, whether or not they are crooked and whether they have prickles. It takes months to learn the system and you can’t just hire part-time workers during the busiest period. I myself only recently learned to sort cucumbers well,” Makoto said.
There are also some automatic sorters on the market, but they have limitations in terms of performance and cost, and small farms don’t tend to use them.
Makoto doesn’t think sorting is an essential task for cucumber farmers. “Farmers want to focus and spend their time on growing delicious vegetables. I’d like to automate the sorting tasks before taking the farm business over from my parents.”
Makoto first got the idea to explore machine learning for sorting cucumbers from a completely different use case: Google AlphaGo competing with the world’s top professional Go player.
“When I saw the Google’s AlphaGo, I realized something really serious is happening here,” said Makoto. “That was the trigger for me to start developing the cucumber sorter with deep learning technology.”
Using deep learning for image recognition allows a computer to learn from a training data set what the important “features” of the images are. By using a hierarchy of numerous artificial neurons, deep learning can automatically classify images with a high degree of accuracy. Thus, neural networks can recognize different species of cats, or models of cars or airplanes from images. Sometimes neural networks can exceed the performance of the human eye for certain applications. . .