https://github.com/manjotkaurgill/agritech
Enter details of your soil and weather, and find best suitable crop for farming. With our advanced AI system, you can make informed decisions and optimize your agricultural practices.
https://github.com/manjotkaurgill/agritech
flask generative-ai insight-generation machine-learning matplotlib mongodb nextjs numpy pandas python scikit-learn seaborn
Last synced: 4 months ago
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Enter details of your soil and weather, and find best suitable crop for farming. With our advanced AI system, you can make informed decisions and optimize your agricultural practices.
- Host: GitHub
- URL: https://github.com/manjotkaurgill/agritech
- Owner: ManjotKaurGill
- Created: 2025-06-03T07:26:10.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-06-03T11:38:49.000Z (8 months ago)
- Last Synced: 2025-07-05T03:08:54.393Z (7 months ago)
- Topics: flask, generative-ai, insight-generation, machine-learning, matplotlib, mongodb, nextjs, numpy, pandas, python, scikit-learn, seaborn
- Language: Jupyter Notebook
- Homepage: https://agritechai.vercel.app
- Size: 4.04 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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
- Manjot Kaur
- Vishawjeet Singh
- Parmeet Kaur
- Arshdeep Singh
- Ratanveer Singh
### Dataset Description
**Source:** [Kaggle Crop Recommendation Dataset](https://www.kaggle.com/datasets/varshitanalluri/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***