Team:Aix-Marseille/Hardware
Detection of the disease
Contents
KILL XYL not only aim to cure the disease caused by Xylella fastidiosa, but also to detect it. Nowadays, the most effective way to detect the bacterium is a method called PCR (Polymerase Chain Reaction) that can detect the presence of this bacterium. However, this method is complex and requires DNA samples from each tree, which will undergo various treatments in the laboratory.
Therefore, we focuse on another method which will allow us to work more easily with hundred of acres of crops. Thus, we though about the detection the first symptoms of disease : the hydric stress. The most revelant way to mesure the hydric stress is with the detection of leaf dryness.
Olive trees are evergreen which means that leaves will not dry naturally. However, dry leaves can be a consequence of other factors than the disease caused X. fastidiosa. Thus our solution serves primarily as a warning device, it's appropriate to the detection of hydric stress through crops. If any is detected, you may take samples and ensure the presence the bacterium.
AS healthy leaves return more infrared light than dry leaf we can calculate the Normalized Difference Vegetation Index (NDVI), which allows to determine the relative level of photosynthesis of a plant. NDVI is calculated by : $$\text{NDVI} = \frac{\text{NIR}-\text{RED}}{\text{NIR}+\text{RED}}$$
The calculated index is between -1 and 1 and is associated with a color scale (1:red and -1:blue)that allows you to observe easily and quickly if the tree is healthy or not.
Detection
First, we needed to build the camera. To do so, we use a Raspberry Pi 2 which will support the different software and a Black Pi camera. The anti-infrared filter was removed from the camera and an addional filter (Blue filter ROSCO#2007) was put in front of it in order to keep only blue and near infrared wavelengths. These wavelengths are relevant for photosynthesis, they will help us to build the NDVI image.
Software
We used Python and OpenCV to calculate our NDVI images. NDVI index computation is done on every pixel of the image to create a new grayscale image, which is then shown with a linear colormap (jet) for a better visual contrast.
