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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)