Contribution de l’imagerie dronique pour la caractérisation des paramètres biophysiques des cultures agricoles
Thanks to information technologies and GNSS (Global Navigation Satellite System), precision agriculture is refining the scale of observation and intervention, from the field to the individual plant. Remote sensing, in particular through satellite imagery, has also made it possible to monitor the crop dynamics, with increasing time frequency and spatial resolution. Crop vigor is a crucial parameter allowing the optimization of inputs, and consequently economic and environmental benefits. Several ways to estimate crop vigor are possible. For a row crop such as corn (Zea Mays L.), aboveground biomass has been favored and will serve as a proxy for vigor. Our study is based on the hypothesis that the recent advent of agricultural UAVs (Unmanned Aerial Vehicles) will pave the way for a better estimation of crop vigor. We assume that the flexibility of the UAV combined with the very high spatial resolution of their images will allow a more accurate monitoring of crop vigor, both in space and time – conditions which are critical for the deployment of precision agriculture. Research over the past 15 years has relied on the UAV to collect images of very high spatial resolution. However, the processing of those images is mainly based on vegetation indices, especially the NDVI. Several questions arise from the above observations: – Is the use of NDVI the most efficient way to exploit the spatial resolution of UAV images? If not, is there a better way to exploit this imagery? – How to respond to time and logistical constraints in the image processing so that it is ultimately adopted by farmers? – Since UAV-borne sensors can be oriented quite freely, what are the effects of the acquisition angles on the results? First, we assessed the relevance of the traditional approach which uses the NDVI index to determine crop parameters. To do that, we compared the UAV platform with the GreenSeeker proximal sensor. The results showed that the UAV is less efficient than the GreenSeeker in estimating corn biomass. We thus moved away from the traditional approach and opted for a method extracting the apparent leaf area, through computer vision techniques applied to RGB images. This method took advantage of the very high spatial resolution offered by UAV images and produced a robust proxy for corn biomass, at the surface (per square meter) and row levels. We also found that the results were not affected by the acquisition angles of the images (nadir and oblique). Therefore, this research opens the perspective to UAV applications in precision agriculture, for the estimation of vigor and other fundamental parameters used in input optimization algorithms. It also offers the possibility to imagine non-UAV based platforms for the acquisition of very high resolution images in the context of fertilization.
UAV Precision agriculture Remote sensing Crop vigor Biomass Corn Fertilization