Mapping infected crops through uav inspection: The sunflower downy mildew parasite case

In agriculture, the detection of parasites on the crops is required to protect the growth of the plants, increase the yield, and reduce the farming costs. A suitable solution includes the use of mobile robotic platforms to inspect the fields and collect information about the status of the crop. Then, by using machine learning techniques the classification of infected and healthy samples can be performed. Such approach requires a large amount of data to train the classifiers, which in most of the cases is not available given constraints such as weather conditions in the inspection area and the hardware limitations of robotic platforms. In this work, we propose a solution to detect the downy mildew parasite in sunflowers fields. A classification pipeline detects infected sunflowers by using a UAV that overflies the field and captures crop images. Our method uses visual information and morphological features to perform infected crop classification. Additionally, we design a simulation environment for precision agriculture able to generate synthetic data to face the lack of training samples due to the limitations to perform the collection of real crop information. Such simulator allows to test and tune the data acquisition procedures thus making the field operations more effective and less failure prone.