From RGB to NIR: Predicting of Near Infrared Reflectance From Visible Spectrum Aerial Images of Crops
Near infrared spectroscopy (NIR) provides rich information in agricultural operations and experiments to determine crop parameters which are not visible to the human eye. Collecting the NIR spectral band requires a multispectral camera which is typically more expensive and has lower resolution than a comparable RGB camera. We investigate image-to-image translation as a means to generate an NIR spectral band from an RGB image alone in aerial crop imagery. Aerial images were captured via a multispectral sensor mounted on an unmanned aerial vehicle (UAV) flown over canola, lentil, dry bean, and wheat breeding trials. A software workflow was created to preprocess raw aerial images creating a dataset suitable for training and evaluating deep learning based band inferencing algorithms. Two different experiments over different crop types in our dataset were conducted to evaluate efficacy in an agricultural context.