Autoencoder Based Image Segmentation for Precision Agriculture
The use of herbicide in agriculture has skyrocketed in the past few decades. This trend has largely been caused by new genetically modified, herbicide resistant crops. Combating the ecological side-effects of chemical overspray as well as easing the economic burden of costly herbicides is where Blue River Technology (BRT) comes in.
Blue River’s flagship product is See & Spray. An intelligent machine that utilizes deep learning to automatically detect and classify crops and weeds on-the-fly and uses precision sprayers to selectively spray weeds, saving vast quantities of chemicals in the process.
This presentation will cover pixelwise semantic segmentation of rear camera image data collected in the field by See & Spray machines and how that information feeds into an in-field calibration for the fluid sprayer system. On the fly detection of spray allows for a closed feedback loop control system where a GPU accelerated semantic autoencoder model works in tandem with the mechanical sprayer system to achieve precision farming.
Andrei is a senior research scientist at Blue River Technology. He is focused on deep learning and computer vision for perception for smarter agricultural machines. Previously, Andrei worked on processing Airbus’ satellite imagery, drones for Lockheed Martin and text localization and semantic understanding of text for Singapore’s Agency for Science Technology and Research. In his spare time Andrei enjoys skiing and hiking.