Identifying Useful Features in Multispectral Images with Deep Learning for Optimizing Wheat Yield Prediction

Since unmanned aerial vehicles have been utilized in plant phenotyping, they have revolutionarily improved its accuracy. In this paper, we introduce a deep learning based approach for optimizing the yield prediction process of spring wheat (triticum aestivum), using multispectral images. We assessed both the temporal features to find the most valuable time to take images, as well as the contribution of spectral bands. We processed full stage multispectral images from four site-years (two sites during two years) of a wheat breeding project, and determined the prediction accuracy of the image-based predicted yields and compared them to the harvested yields taken in the field. The results compared the wheat images throughout the season and validated the most crucial flying times for acquiring images were at late-heading, late-flowering, dough-development, and harvesting stages. The two most useful colour-bands for yield prediction were red and red-edge. We found that removing these bands significantly decreased the prediction correctness. The results of this research could be a tool for the development of more efficient sensors and strategies for data collection in plant phenotyping.