AgriTech

A project completed during Intel® Unnati Industrial Training Program 2024.

Introduction

In today's data-centric world, organizations face the challenge of not only storing vast amounts of structured data but also extracting meaningful insights to drive decision-making. This project aims to address this challenge by developing an AI-based solution capable of effectively analyzing and interpreting structured data.

Objectives

1. Represent Knowledge: Use advanced techniques to structure and highlight critical information and relationships within the data.
2. Generate Insights: Analyze the data to identify patterns, trends, and anomalies, offering valuable insights that are not easily recognized through manual analysis.
3. Aid Decision-Making: Present the generated insights in a user-friendly manner to enable stakeholders to make informed decisions based on accurate and comprehensive data analysis.

Team Members

• Vishawjeet Singh
• Manjot Kaur
• Parmeet Kaur
• Arshdeep Singh
• Ratanveer Singh

Dataset Description

Source: Kaggle Crop Recommendation Dataset

Methodology

• Data Cleaning: Ensured no missing values or duplicates.
• EDA: Visualized data distribution and relationships.
• Preprocessing: Label encoding and feature scaling.
• Model Training: Random Forest Classifier, evaluated with accuracy scores, and tuned with RandomizedSearchCV.

Tools Used

• Libraries: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn,
• Platforms: Google Colab, Next.js, Flask, Vercel

Results

• High accuracy in crop prediction.
• Visualizations: Histograms, boxplots, heatmaps, bar plots, and confusion matrix.
• Insights on optimal crop conditions and critical features.
• Predict best crop according to user soil and weather conditions.

Run App on your computer

Simply visit https://agritechai.vercel.app/ or follow following methods to run app on your computer.

Backend (Flutter)

• Open folder /src/Backend in your code editor.
• Create new python environment:

  python -m venv env

• Activate environmet by command:

  .\env\Scripts\activate

• Install required packages or Scripts:

  pip install -r .\requirements.txt

• Run Flask backend using command"

  flask --app app run

FrontEnd (Next JS)

• Install Node js on your machine. https://nodejs.org/en
• Open folder /src/FrontEnd in your code editor.
• In terminal run follwing commands:

  npm install

• In file /src/FrontEnd/configurations/address.ts, Replace "https://agritechbackendflask.onrender.com" with "http://127.0.0.1:5000".
• Run your app with command:

  npm run dev