#earth, #leafmealone, #intermediate
Model, Imagery, Platform, Data Visualization, Citizen Science
The U.S. Department of Agriculture describes the effect of ozone pollution on plants as follows: Ozone is formed in the troposphere when sunlight causes complex photochemical reactions involving oxides of nitrogen (NOx), volatile organic hydrocarbons (VOC) and carbon monoxide that originate chiefly from gasoline engines and burning of other fossil fuels. Woody vegetation is another major source of VOCs. NOx and VOCs can be transported long distances by regional weather patterns before they react to create ozone in the atmosphere, where it can persist for several weeks.
Ozone enters leaves through stomata during normal gas exchange. As a strong oxidant, ozone (or secondary products resulting from oxidation by ozone such as reactive oxygen species) causes several types of symptoms including chlorosis and necrosis. It is almost impossible to tell whether foliar chlorosis or necrosis in the field is caused by ozone or normal senescence. Several additional symptom types are commonly associated with ozone exposure, however. These include flecks (tiny light-tan irregular spots less than 1 mm diameter), stipples (small darkly pigmented areas approximately 2-4 mm diameter), bronzing, and reddening.
Ozone symptoms usually occur between the veins on the upper leaf surface of older and middle-aged leaves, but may also involve both leaf surfaces (bifacial) for some species. The type and severity of injury is dependent on several factors including duration and concentration of ozone exposure, weather conditions and plant genetics. Ozone exposure can lead to lower crop yields. The results of studies show that dicot species (soybean, cotton and peanut) are more sensitive to yield loss caused by ozone than monocot species (sorghum, field corn and winter wheat). The Ozone-Induced Foliar Injury Guide (in the resources section) includes many details related to this challenge.
We’re so pleased with the results from Space Apps 2014, we’re offering this challenge again.
Ground-level ozone causes more damage to plants than all other air pollutants combined, including damage to food crops. (See the effect of ozone pollution on plants for background.) One way to see the effects of this air pollution is to evaluate the damage to leaves, also called stippling. Develop a tool to quantify and classify the amount of injury to the individual leaves of ozone-sensitive plants.
- Take a picture, then calculate and display percent results. Advanced options would add display of surface ozone data for that location for the last five days. If possible, you can add or display a satellite image for that area to provide context
- (i.e., Landsat Leaf Area Index or Normalized Difference Vegetation Index).
- The app could:
- guide a user through a protocol for taking a single leaf image;
- acquire a picture of a leaf through a smartphone camera;
- use the colors in the photo to calculate the area of the leaf and
- determine the fraction of the leaf that has been injured by ozone.
- Present the results of the analysis using the classification described on page 69 of the Ozone-Induced Foliar Injury Guide (link below) and present the last 5 days of surface ozone for the user’s location.
Sample Resources(Participants do not have to use these resources, and NASA in no way endorses any particular entity listed).
- Ozone-Induced Foliar Injury Guide http://science-edu.larc.nasa.gov/ozonegarden/pdf/Bio-guide-final-3_15_11.pdf
- Ozone Garden website http://science-edu.larc.nasa.gov/ozonegarden/
- MY NASA DATA: http://mynasadata.larc.nasa.gov
- LANDSAT 8: http://landsat.usgs.gov/landsat8.php
- ASTER: http://asterweb.jpl.nasa.gov/data.asp
- EO-1: http://eo1.usgs.gov/
- MODIS: http://modis.gsfc.nasa.gov/gallery/
The following projects are solving this challenge:
Develop an app given an image of a leaf and it will classify it Visit Project
Acquire image through smartphone. Transmit image to a Matlab server. Create a database of leaves with healthy leaves, ozone afflicted leaves and other infectious leaves. Run pre-porocessing and color based segmentation on leaves for classification of healthy, non-healthy leaves. Possibly id... Visit Project
Although the leaves serve as biomarkers for tropospheric ozone, scientist don't have an accurate idea of the extent of damage that has tropospheric ozone has done around the world. Data collection of large numbers of affected plants help to have a notion of whre the negative impact is generatin... Visit Project
The aim of this project is to aid environmentalists, botanists, farmers and biological scientists in identifying and classifying damage to leaves caused by ground ozone. This application allows the user to take a picture of a potentially damaged leaf and the user to give a keyword based on the us... Visit Project
A bracelet which measures vital signs and alerts to a second wired device. Visit Project
Acquire an image of an Ozone-sensitive leaf through a mobile phone camera, analyze the image and classify the individual pixels, display percent results and an image showing the damaged parts of the leaf. Visit Project
GET LEAVES IMAGES THROUGH SMARTPHONES AND SEND THEM TO A DATABASE IN A WEBPAGE SELECT THE PERCENTAGE OF DAMAGE THAT THE LEAF PRESENTS AND GIVE A QUANTITY OF OZONE POLLUTION AS A RESULT THEN COMPARE THE RESULTS WITH PREVIOUS ANALYSIS DONE IN THE ZONE Visit Project
Ozone 3 is a simple to use app for hobby gardeners and terrace farmers helping them to know the effect of ozone on their crop leafs. Also the app provides Normalized Difference Vegetation Index, which helps in knowing the chlorophyll level and nitrogen content of the plants. This helps in knowing... Visit Project
O3 Injury Detection
Our project aims to automatically detect leave injury from leaf pictures taken from a mobile camera. The images must have a uniform background (say a leaf agsint a sheet of paper) for easier segmentation. Segmentation is achieved by clustering the pixels is leaf/no-leaf clusters, using the color ... Visit Project
#Mobile App The Mobile App allows you to take a leaf's photo. Then the app gives an ID and requires a user to fill a form which includes: - Crop name - Dropped Leaf? - Zone: Urban or Rural? Moreover the app automatically adds the date, time and GPS location. Finally it sends the dat... Visit Project
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