Computer vision has quietly emerged as a major pillar in the foundation of AI. Image analysis has grown dramatically over the past few years and with greater access to the processing power of the Cloud. Object recognition is now astonishingly precise, and now with implementation of deep neural networks, error rates are less than 2%.
When Dr. Fei Fei Li began constructing the ImageNet database, smart detection of objects was limited to yes / no options. (Yes, hot dog. No, not hot dog.) It took almost 3 years for her team to assemble and organize over 3.2 million images for the initial data set of imagery. Eventually the set ballooned to 15 million images to organize the world’s objects into a language machines can understand. This paved the way for other datasets and refined the speed and efficiency with which machines could learn. Now instead of requiring thousands of images of an object, machines can be taught with a few hundred.
Tesla’s Sr. Director of Artificial Intelligence Andrej Karpathy stated at a recent Tesla press conference that visual recognition is absolutely necessary for their push into autonomy. Each Tesla you see on the road today depends on deep neural networks interpreting HD footage shot by the 8 onboard cameras in real time. This enables the car to understand the environment it is self-driving in. That technology of rapidly interpreting and understanding the world through visual data while in motion is now here.
Farmwave has always used image analysis to empower its users. We were the first to leverage image processing algorithms to count the kernels on an ear of corn. This helped growers determine yield when factoring in stand counts. Soon we were tapping into cloud based systems to power our image recognition of diseases on crops. We call this detection tool our CORE (Cloud Optimized Recognition Engine).