Augmentation of Sunflower-Weed Segmentation Classification with Unity Generated Imagery Including Near Infrared Sensor Data

This paper presents a solution to create synthetic datasets for deep learning training of convolutional neural networks (CNNs) for plant-weed classification. We use the Unity game engine to create simulated procedural fields of sunflowers and weeds images. The visual imagery is generated by the photo realistic real time rendering engine in Unity. Moreover, we include the regular red, green, and blue (RGB) channels, plus the near infrared (NIR) channel data. This is done by including the aligned textures from both the RGB and the NIR channel separately, since Unity does not simulate NIR illumination.