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Community Blog Productive Agriculture: Grow Your Crops Better with AI-Based Climate Prediction

Productive Agriculture: Grow Your Crops Better with AI-Based Climate Prediction

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.

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.

Project Introduction

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.

1

Solution and Project Value

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.

Technology Highlights

We used several technologies in this project, including Flask, Python, Html, CSS, JavaScript, bootstrap, Power BI and Heroku.

  • Machine Learning Models (We choose the model as per the accuracy):

We used Random Forest Logistic Regression, Decision Tress Naïve Bayes SVM (Support Vector Machine), etc.

  • Algorithm (We make these algorithms by ourselves):

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).

About the Developer

  • Abdur Rafay, National University of Computer & Emerging Sciences (FAST-NUCES) (7th Semester) Machine Learning Engineer and Full Stack Web Developer
  • Furqan Ahmed, National University of Computer & Emerging Sciences (FAST-NUCES) (7th Semester) Machine Learning Engineer and Full Stack Web Developer
  • Muhammad Ahsan, National University of Computer & Emerging Sciences (FAST-NUCES) (7th Semester) Full Stack Web Developer and UI/UX Designer

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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