Above-ground biomass, along with chlorophyll content and leaf area index (LAI), is a key biophysical parameter for crop monitoring. Being able to estimate biomass variations within a field is critical to the deployment of precision farming approaches such as variable nitrogen applications.

With unprecedented flexibility, Unmanned Aerial Vehicles (UAVs) allow image acquisition at very high spatial resolution and short revisit time. Accordingly, there has been an increasing interest in those platforms for crop monitoring and precision agriculture. Typically, classic remote sensing techniques tend to rely on a vegetation index – such as the popular Normalized Difference Vegetation Index (NDVI) – as a proxy for plant biophysical parameters. However, when applied to UAV imagery, those approaches do not fully exploit the greater details provided by high resolution.

The purpose of this research is to develop a procedure for assessing above-ground biomass based on the analysis of very high resolution RGB imagery acquired with a UAV platform. A small consumer-grade UAV (the DJI Phantom 3 Professional) with a built-in RGB camera was flown over an experimental corn (Zea mays L.) field. A series of images were acquired in summer 2017 at very low altitudes, resulting in milli-resolution imagery (images with less than 1 cm per pixel). Two modes of image acquisition were performed: in a grid pattern at an altitude of 10m AGL (above ground level) for generating orthomosaics, and in a stationary mode at a height of 2.9m AGL. For stability reasons, the latter mode was simulated by a low-altitude platform hung on a zip-line.

Image acquisitions were repeated in time during the early stages of corn growth, covering phenological stages from V2 to V8. Oblique imagery was also acquired in order to evaluate the effect of viewing angle. Field measurement campaigns were carried out in order to provide quantitative measurements of some biophysical parameters, including plant fresh biomass, plant dry biomass, plant height, leaf fresh biomass and leaf dry biomass. The method proposed in this study is based on computer vision, which allowed extracting leaf projected area from the images for estimating biomass and detecting differences in corn growth. Using UAV-derived imagery to extract information on biomass proves to be a cost-effective means for monitoring crop biomass spatially and temporally.