{"id":28501832,"url":"https://github.com/manjotkaurgill/agritech","last_synced_at":"2026-04-12T13:02:50.421Z","repository":{"id":296982910,"uuid":"995243851","full_name":"ManjotKaurGill/AgriTech","owner":"ManjotKaurGill","description":"Enter details of your soil and weather, and find best suitable crop for farming. 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This project aims to address this challenge by developing an AI-based solution capable of effectively analyzing and interpreting structured data.\n\n### Objectives\n1. **Represent Knowledge:** Use advanced techniques to structure and highlight critical information and relationships within the data.\n2. **Generate Insights:** Analyze the data to identify patterns, trends, and anomalies, offering valuable insights that are not easily recognized through manual analysis.\n3. **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.\n\n### Team Members\n- Manjot Kaur\n- Vishawjeet Singh\n- Parmeet Kaur\n- Arshdeep Singh\n- Ratanveer Singh\n\n### Dataset Description\n**Source:** [Kaggle Crop Recommendation Dataset](https://www.kaggle.com/datasets/varshitanalluri/crop-recommendation-dataset)\n### Methodology\n- **Data Cleaning:** Ensured no missing values or duplicates.\n- **EDA:** Visualized data distribution and relationships.\n- **Preprocessing:** Label encoding and feature scaling.\n- **Model Training:** Random Forest Classifier, evaluated with accuracy scores, and tuned with RandomizedSearchCV.\n\n### Tools Used\n- **Libraries:** Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn,\n- **Platforms:** Google Colab, Next.js, Flask, Vercel\n\n### Results\n- High accuracy in crop prediction.\n- Visualizations: Histograms, boxplots, heatmaps, bar plots, and confusion matrix.\n- Insights on optimal crop conditions and critical features.\n- Predict best crop according to user soil and weather conditions.\n\n## Run App on your computer\nSimply visit **https://agritechai.vercel.app/** or follow following methods to run app on your computer.\n### Backend (Flutter)\n- Open folder **/src/Backend** in your code editor.\n- Create new python environment:\n    ##### ***python -m venv env***\n- Activate environmet by command:\n    ##### ***.\\env\\Scripts\\activate***\n- Install required packages or Scripts:\n    ##### ***pip install -r .\\requirements.txt***\n- Run Flask backend using command\"\n    ##### ***flask --app app run***\n\n### FrontEnd (Next JS)\n- Install Node js on your machine. https://nodejs.org/en\n- Open folder **/src/FrontEnd** in your code editor.\n- In terminal run follwing commands:\n    ##### ***npm install***\n- In file **/src/FrontEnd/configurations/address.ts**, Replace \"https://agritechbackendflask.onrender.com\" with \"http://127.0.0.1:5000\".\n- Run your app with command:\n    ##### ***npm run dev***\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmanjotkaurgill%2Fagritech","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmanjotkaurgill%2Fagritech","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmanjotkaurgill%2Fagritech/lists"}