TOWARD A LOW-COST, MULTISPECTRAL, HIGH ACCURACY MAPPING SYSTEM FOR VINEYARD INSPECTION

Abstract

Confronted with the climate change challenge and the territorial constraints, agriculture has to modernize itself. The use of georeferenced data and remote-sensing imagery is a major step in this direction. This precision mapping of crops requires powerful and accurate acquisition systems, while remaining financially attractive. The development of multispectral sensors and low-cost GNSS makes it possible to consider systems that will be able to map at the plant scale. However, these positioning systems do not yet guarantee a precise overlap of data acquired at different times. Thus, we propose in this paper a method to register terrestrial image data, acquired on vineyard plots. Our method seeks to avoid image registration problems, such as illumination changes, by detecting the vine stocks, reconstructing them in 3D, and registering them individually. The 3D detection method is based on an image-based object detection method (Faster R-CNN) and a structure-from-motion reconstruction of object-masked images. The results that we obtained on a vineyard plot, allowed us to validate the method, with a precision of less than 10 cm, making it possible to map the vine by stock.