Artificial intelligence technology doesn't just have to solve grand challenges. Sometimes, it can tackle decidedly everyday problems -- like, say, improving a cucumber farm. Makoto Koike has built a cucumber sorter that uses Google's TensorFlow machine learning technology to save his farmer parents a lot of work. The system uses a camera-equipped Raspberry Pi 3 to snap photos of the veggies and send the shots to a small TensorFlow neural network, where they're identified as cucumbers. After that, it sends images to a larger network on a Linux server to classify the cucumbers by attributes like color, shape and size. An Arduino Micro uses that info to control the actual sorting, while a Windows PC trains the neural network with images.
It's not a perfect system, at least right now. Koike estimates that it takes about 2-3 days to train the sorting AI, even using very low-resolution (80 x 80) pictures. And even the 7,000 photos Koike used for that training probably weren't enough. While the sorter recognized 95 percent of test images, real-world sorting dipped to about 70 percent. Having said that, it's not the immediate results that matter. The technology can get better, and it hints at a future where robotic farm equipment handles many mundane tasks that previously required a human's watchful eye.