Inspiration
Most of the farmers in developing countries only know spraying and irrigation as forms of maintaining their crops and
farms and these are many times expensive solutions to them given their low incomes. AgriKop is inspired by the
prevention is better than cure mantra to help these low resourced farmers and the fact that since many of these
farmers have minimal to no education, visual representations of information will be easy for them to digest than
mathematical figures.
What it does
The app helps farmers keep track of various environmental factors that contribute most to their farming industry
especially temperature, rainfall and humidity and alert the farm owner in case of predicted extremes in any of these
factors.
How I built it
The app visuals and layout are built with dash and a machine learning model that predicts changes in environmental
factors. The model is built with pytorch and python helped with the initial analysis of the data.
Challenges I ran into
The main challenge was getting actual environmental data to test build my predictive model.
Accomplishments that I'm proud of
Being able to build the application to where it now.
What I learned
What's next for AgriKop
I hope to test the application and evaluate its performance in production and I also hope to scale the machine learning model to use satellite imagery to be able to predict environmental changes in a given farm area. This is because satellite imagery is cheaper in the long run than buying sensors for every farm that uses our product.
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