Unmanned Aerial Vehicle (UAV)- Based Remote Sensing for Crop Phenotyping.

Leaf Area Index (LAI) and fractional vegetation cover (f_c) are key features to characterize the crop growth that can support farmers decision-making process. We demonstrate the applicability of UAV multispectral data for estimating LAI and f_c. A UAV equipped with a multispectral camera (GEMS, Sentek Systems) was used for image acquisition over different varieties of winter wheat and bioenergy grass, grown in trial micro-plots during 2016 growing season. The altitude for image acquisition was 120 ft. Simultaneously with the UAV flights, we conducted ground sampling for plant leaf area index (LAI) and fractional ground cover (f_c). We pre-processed the images to extract Normalized Different Vegetation Index (NDVI) from each plot. The results show that (a) there is a good correlation between LAI and NDVI extracted from UAV images and (b) NDVI is significantly correlated with f_c. These results demonstrate the potential of low-cost effective UAV images in crop growth assessment during growing season.