Presently, precision agriculture uses remote sensing for the mapping of crop biophysical parameters with vegetation indices in order to detect problematic areas, and then send a human specialist for a targeted field investigation. The same principle is applied for the use of UAVs in precision agriculture, but with finer spatial resolutions. Vegetation mapping with UAVs requires the mosaicking of several images, which results in significant geometric and radiometric problems. Furthermore, even at such resolutions, it is still not possible to precisely identify the nature of the detected stresses. The concept proposed here aims to use UAVs for precise and automated pest detection and identification with images acquired a few meters above the crop canopy, at millimetric resolution.

The image processing is based on artificial intelligence (deep learning) computer vision methods. These methods are trained with images collected for different crops and symptoms. The UAV image acquisitions calendar is optimized using a bioclimatic model that evaluates disease risk. The spatial acquisition plan prioritizes areas with persistent moisture, where the probability of pest presence is higher. These areas could be determined using optical or SAR satellite imagery.

This approach was applied to detect diseases in a vineyard (mildew), potato beetles and weeds (in lettuce, carrot and onion fields). All experimental fields were located in Quebec, Canada. Results show that the application of the deep learning technique to crop canopy UAV images can reach a success rate above 90%, which demonstrates the potential of this approach. Thus, the proposed concept is a major innovation in the application of UAVs in agriculture. It will allow the effective control of pests by optimizing pesticide use while reducing the waste of resources and the harmful effects of chemical products.