Difference between revisions of "Team:Aix-Marseille/Hardware"

 
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{{Aix-Marseille|title=Detection of the disease|toc=__TOC__}}
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{{Aix-Marseille|title=Detection of the disease|toc=__noTOC__}}
  
 
[[File:T--Aix-Marseille--transmission chain-1.png|right|400px|The hydric stress detection method.]]
 
[[File:T--Aix-Marseille--transmission chain-1.png|right|400px|The hydric stress detection method.]]
  
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'''KILL XYL''' not only aim to cure the disease caused by [[Team:Aix-Marseille/Xylella_fastidiosa|''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 [[Team:Aix-Marseille/Xylella_fastidiosa|''Xylella fastidiosa'']]. However, this method is complex and requires DNA samples from each tree, which will undergo various treatments in the laboratory.
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[[Team:Aix-Marseille/Project|'''KILL XYL''']] aims not only to cure the disease caused by [[Team:Aix-Marseille/Xylella_fastidiosa|''Xylella fastidiosa'']], but also to detect it. Nowadays, the most effective way to detect the bacteria is a method called PCR (Polymerase Chain Reaction).  
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However, this method is complex and requires DNA samples from trees and lengthy and complex laboratory treatments.
  
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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.  
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Therefore, we focused on another method which will allow us to work more easily in the open with hundreds of acres of crops.  
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For this, we thought about detecting the first symptoms of the disease: hydric stress.  
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The easiest way to measure the hydric stress is by assessing leaf dryness.  
  
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[[File:T--Aix-Marseille--refraction spectrum leaves-1.png|400px|right]]
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[[File:T--Aix-Marseille--refraction spectrum leaves-1.png|400px|right|thumb|Healthy leaves, because of photosynthesis, refract more infrared (IR) light than dry leaves.]]
  
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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[[Team:Aix-Marseille/Xylella_fastidiosa|''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 [[Team:Aix-Marseille/Xylella_fastidiosa|''X. fastidiosa'']].
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Olive trees are evergreen which means that leaves will not dry naturally.  
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However,  dry leaves can be a consequence of other factors than the disease caused by [[Team:Aix-Marseille/Xylella_fastidiosa|''X. fastidiosa'']].  
 +
Thus our solution serves primarily as a warning device, it's appropriate to the detection of hydric stress in crops.  
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If any is detected, then DNA samples should be taken to verify the presence of the bacteria.
  
 
[[File:T--Aix-Marseille--NDVI comparison-1.jpg|400px|right|thumb|K-State Research and Extension
 
[[File:T--Aix-Marseille--NDVI comparison-1.jpg|400px|right|thumb|K-State Research and Extension
 
Soybean NVDI photo]]
 
Soybean NVDI photo]]
  
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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 : $$\text{NDVI} = \frac{\text{NIR}-\text{RED}}{\text{NIR}+\text{RED}}$$
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As healthy leaves reflect more infrared light than dry leaves we can calculate the Normalized Difference Vegetation Index (NDVI), which allows determining the relative level of photosynthesis of a plant. NDVI is calculated by : $$\text{NDVI} = \frac{\text{NIR}-\text{RED}}{\text{NIR}+\text{RED}}$$
  
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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.  
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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==
 
==Detection==
  
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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.
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First, we built the camera.  
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For this, we used a Raspberry Pi 2 which supports the necessary acquisition and treatment software and a Black Pi camera.  
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The infrared blocking filter was removed from the camera and replaced with a Blue filter (ROSCO#2007).
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This modification allows us to detect the NIR and RED light necessary to construct the NDVI image.
  
 
==Software==
 
==Software==
  
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To make a NDVI image, we used a library that allows image processing. This library allows the detection of objects, faces, shapes. It can be used with various programming languages, mainly C ++ and python. It is the last one that we have used for its portability, which will simplify the development.
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[[File:T--Aix-Marseille--NDVI-process.png|right|500px|thumb|]]
  
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In the computer memory, the images are coded in RGB, i.e. each color, red, green and blue, is coded on 1 byte and represents the intensity of the color. In a normal camera, the colors are captured and restored, the resulting image is in accordance with natural colors. In our case, the filter removes the red component, and is replaced by infrared. The resulting image of this montage is in "false colors" because it is composed of green and blue, visible by a human eye with infrared which is normally impossible to see. The NDVI index can be calculated for each pixel of the image.
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We used Python and OpenCV to calculate our NDVI images.  
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NDVI index computation is done pixel by pixel on the image to create a new grayscale image, which is then shown with a linear colormap (jet) for a better visual contrast.
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With the index of each pixel, we create a new grayscale image. The image is the visual representation of the NDVI index. However, the human eye cannot easily discern the gray levels, but more easily the color variations. We will use the OpenCV library to apply a color scale to our image. We apply the color colormap_jet.
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Latest revision as of 02:29, 2 November 2017

Detection of the disease

The hydric stress detection method.

KILL XYL aims not only to cure the disease caused by Xylella fastidiosa, but also to detect it. Nowadays, the most effective way to detect the bacteria is a method called PCR (Polymerase Chain Reaction). However, this method is complex and requires DNA samples from trees and lengthy and complex laboratory treatments.

Therefore, we focused on another method which will allow us to work more easily in the open with hundreds of acres of crops. For this, we thought about detecting the first symptoms of the disease: hydric stress. The easiest way to measure the hydric stress is by assessing leaf dryness.

Healthy leaves, because of photosynthesis, refract more infrared (IR) light than dry leaves.

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 by X. fastidiosa. Thus our solution serves primarily as a warning device, it's appropriate to the detection of hydric stress in crops. If any is detected, then DNA samples should be taken to verify the presence of the bacteria.

K-State Research and Extension Soybean NVDI photo

As healthy leaves reflect more infrared light than dry leaves we can calculate the Normalized Difference Vegetation Index (NDVI), which allows determining 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 built the camera. For this, we used a Raspberry Pi 2 which supports the necessary acquisition and treatment software and a Black Pi camera. The infrared blocking filter was removed from the camera and replaced with a Blue filter (ROSCO#2007). This modification allows us to detect the NIR and RED light necessary to construct the NDVI image.

Software

T--Aix-Marseille--NDVI-process.png

We used Python and OpenCV to calculate our NDVI images. NDVI index computation is done pixel by pixel on the image to create a new grayscale image, which is then shown with a linear colormap (jet) for a better visual contrast.