Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat

Crop production needs to increase in a sustainable manner to meet the growing global demand for food. To identify crop varieties with high yield potential, plant scientists and breeders evaluate the performance of hundreds of lines in multiple locations over several years. To facilitate the process of selecting advanced varieties, an automated framework was developed in this study. A hyperspectral camera was mounted on an unmanned aerial vehicle to collect aerial imagery with high spatial and spectral resolution. Aerial images were captured in two consecutive growing seasons from three experimental yield fields composed of hundreds experimental plots (1x2.4 meter), each contained a single wheat line. The grain of more than thousand wheat plots was harvested by a combine, weighed, and recorded as the ground truth data. To leverage the high spatial resolution and investigate the yield variation within the plots, images of plots were divided into sub-plots by integrating image processing techniques and spectral mixture analysis with the expert domain knowledge. Afterwards, the sub-plot dataset was divided into train, validation, and test sets using stratified sampling. Subsequent to extracting features from each sub-plot, deep neural networks were trained for yield estimation. The coefficient of determination for predicting the yield of the test dataset at sub-plot scale was 0.79 with root mean square error of 5.90 grams. In addition to providing insights into yield variation at sub-plot scale, the proposed framework can facilitate the process of high-throughput yield phenotyping as a valuable decision support tool. It offers the possibility of (i) remote visual inspection of the plots, (ii) studying the effect of crop density on yield, and (iii) optimizing plot size to investigate more lines in a dedicated field each year.