Integrated Imagery Approaches for Plant Growth Monitoring in Corn Via UAS.

Detailed spatial and temporal data becomes critical to overcome sustainable and efficient management practices. Conventional methods (via plant sampling procedure) for estimating biomass are labor-intensive, time-consuming, and related to small-scale. Recent incursion of small-unmanned aerial systems (sUAS) seem promising for bridging a gap in the scale issue. The main goal of the project is to evaluate ultra-high spatial resolution data by integrating aerial SFM (Structure From Motion) and multispectral data to monitor seasonal growth and yield. The experiment was located at Ashland Bottoms Research Farm, Kansas State University (Manhattan, KS). Four experiments were carried out during 2016 growing season. The studies were related to investigation of fertilizer N rates, hybrid variation, plant density, and stand uniformity (random-gaps) utilized as a base line for spatial and temporal variability generation for SFM and multispectral data evaluation. Six flight missions were performed during the growth season. Photoscan and ArcGIS 10.3 software were used for imagery stitching and data extraction. Preliminary 2015 data of plant height estimated from SFM data versus ground truth at flowering stated a correlation of 0.83. Several biophysical plant traits (e.g. stand counts, plant height, biomass, stalk diameter, among others) and phenology stages are been collected during 2016 in the experiment to evaluate goodness of fit of aerial data. Crop growth and functional status at varying growth stages (temporal-scale) are utilized for biomass and yield spatial prediction; a final yield validation will be implemented from data collected from the field studies.