Predicting Crop Modal Price Volatility Using Normalized Difference Vegetation Index (NASA NDVI)
Channel: Frank The Tank
Date: 17/05/2021

Here is my lightning talk at the University of Chicago! I use Graph Theory and Machine Learning to create an equitable solution for marginal tomato farmers in Kalikiri, Andhra Pradesh. As the recent farmer's protests have shown, poor and marginal farmers have the most to lose from recent changes in the farm laws. This algorithm seeks to remedy some of those changes by accurate forecasting and remote sensing of crop productivity.  If you would like to reproduce this work contact me at : fmendes at uchicago dot edu 

Relevant videos : 
https://www.youtube.com/watch?v=fj6AzxFWFXU
https://timesofindia.indiatimes.com/videos/in-depth/crisis-in-indian-agriculture-explained-why-farmers-are-destroying-their-tomato-crop/videoshow/82688506.cms
https://www.youtube.com/watch?v=OS9xfLmtlME
 https://www.youtube.com/watch?v=Jjlng97lg7Y



This algorithm uses remote sensing data and road networks to create an algorithm that would allow farmers to detect, in real time, the volumes of harvest at a district level as well as to detect any potential fluctuations in price. We borrow heavily from signal-processing and graph theory to create accurate yet simple measures of volatility in harvest volumes and logistical connectivity. Ultimately, the democratic distribution of these methods to rural communities will allow an accurate assessment of risk,  as well as, influence village level strategic decision making.