Summary

Project Summary
United States is a leader in agriculture and a big food donor to the world. Last year, United Nations Food and Agriculture Organization reported 925 million people in the world were under or malnourished. The spreading of urbanization causes shrinking in farmlands. This means fighting against world hunger will require higher yield and better crop management.

My state, Illinois, is the 2nd largest soybean producer in the US. Early crop health assessment can help farmers maximize harvest yield and minimize fertilizer and pesticide application. Remote sensing imagery by NASA satellites that orbit daily, provide greenness index of any crop precisely at any point.
My research studied the correlation among the greenness indices of plant leaves, along with plant height, soil parameters, and harvest yield in a growing season. I used these 3 methods to measure greenness:

1.      The conventional spectrophotometric method chemical extraction

2.      The Chlorophyll Content Index (CCI) method fast, nondestructive, handheld

3.      And the Remote Sensing method – where NASA’s satellites produce Normalized Difference Vegetation Index and Enhanced Vegetation Index (NDVI and EVI) values.


My big question is how does the remote sensing method compare with the ground data and can it help manage a farm better? To find the answer, I collected leaf and soil samples of a soybean field regularly in growing season and got harvest yield monitor data at the end.

In my project, the dependent variables are the amount of chlorophyll and soil parameters. Independent – is time in season. And controls are using the same measuring devices and calibrating machines.
When compared, the results showed a positive correlation between the satellite and ground data. These findings indicate that remote sensing can assess crop health and stress in-season, therefore help growers better manage their farms as well as the environmental impact.

Munia's Summary Video - Assessment of Crop Health Using NDVI and EVI

 


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