This project is from the team Vortex, which was awarded with the Third Prize in the Global AI Innovation Challenge 2021 - Intelligent Weather Forecast for Better life.
The primary problems that our application addresses are how greenhouse gases affect global climate conditions, how both of these are correlated, and how they are affecting the yield of the crops.
Current project scope is limited to Indian data. It can further be expanded on Global Level once global data is provided to our team.
In past, global warming have a great impact on climate change due to which the agricultural industry has suffered a lot, Therefore there is a need of such product which can analyze and determine a profitable solution in such conditions.
Our product will not only improve the agricultural department by predicting the future climate condition and determining the change in crop production which is caused by the climate change, but also will provide the possible crops that can be grown in that climatic condition. This is done by giving the market value of each potential crop in the past years. The information provided by our product will allow agricultural workers to choose which crop should be grown in order to ensure maximum profit.
Our system will provide an analysis of how greenhouse gases and climate are correlated with each other. The conclusion will be drawn on the basis of that analysis. After completing the analysis we will be using the gases as features to train our machine learning model (Model 1) and predicting the future climate condition. Secondly, we will be using climate conditions as a feature to train our machine learning (Model 2) and predict crop production in specific parts of that area. In order to determine the crop that should be grown to maximize profit, we will refer to our dataset, which contains the market value of each crop over the previous years.
We used Random Forest Logistic Regression, Decision Tress Naïve Bayes SVM (Support Vector Machine), etc.
Ranking Algorithm Analysis Algorithm Implementation:
First we have trained our machine learning models using python provided by scikit-learn-lib. Then we integrated those models in our website using flask (framework). Then we deployed our website on Heroku free server. We have used Power-BI for our analysis part (Not in used in this submission but provided documentation on how ECS and machine learning platform for AI can be integrated in future).
We all are students of FAST-NUCES currently pursuing our Bachelors in Computer Science. The complete project is built by our team.
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