https://github.com/mmerlyn/tomato-price-predictor
Machine learning web app that predicts tomato market prices in Karnataka, India to help farmers optimize crop planning and sales decisions
https://github.com/mmerlyn/tomato-price-predictor
agriculture-research css3 flask-application gradient-boosting html machine-learning price-prediction-model python
Last synced: 4 months ago
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Machine learning web app that predicts tomato market prices in Karnataka, India to help farmers optimize crop planning and sales decisions
- Host: GitHub
- URL: https://github.com/mmerlyn/tomato-price-predictor
- Owner: mmerlyn
- License: mit
- Created: 2024-11-10T02:21:08.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2026-02-08T01:39:26.000Z (6 months ago)
- Last Synced: 2026-04-12T03:38:27.259Z (4 months ago)
- Topics: agriculture-research, css3, flask-application, gradient-boosting, html, machine-learning, price-prediction-model, python
- Language: Jupyter Notebook
- Homepage: https://drive.google.com/file/d/18bQ2IIrC1MLoron7oFICsjhk-EbTgm0A/view?usp=drive_link
- Size: 10.3 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🍅 Analysis of Tomato Prices in Karnataka
This project analyzes tomato prices in Karnataka from 2016 to 2018 and forecasts future prices using a machine learning model. The aim is to help farmers make informed decisions about crop planning and sales based on historical pricing patterns and supply trends.
[🌐 Demo](https://drive.google.com/file/d/18bQ2IIrC1MLoron7oFICsjhk-EbTgm0A/view?usp=sharing)
## 💡 Motivation & Impact
Analysis of tomato prices in Karnataka from 2016 to 2018/4
- It is heartbreaking to learn that farmers in India sometimes have to let their harvests rot in the field, due to low market prices.
- This is more commonly seen in tomato harvests, where during some particular months, due to very low market prices, the cost of harvesting and transporting is higher than the expected return.
- Hence most farmers let the fruits to rot/throw them on roads, let cattle eat them, etc.
- This problem is due to the supply demand imbalance, where increase in supply, reduces demand, leading to low prices.
- Using machine learning techniques, we can predict the expected price for tomatoes, at a particular market, when supply is a certain number of tonnes.
- This knowledge, can help farmers, plan ahead, and may be opt for a different crop, if the predicted selling price is low.
## Dataset
Source: [Kaggle - Vegetable Price Tomato](https://www.kaggle.com/vinayreddy4034/vegetablepricetomato)
- 40 markets across Karnataka
- Daily price records from 2016-2018
- Features: Market Area, Date, Tonnes, Min/Max/Modal Price
## Technologies Used
- Python
- Pandas, NumPy
- Scikit-learn (Gradient Boosting)
- Matplotlib, Seaborn (Visualization)
- Flask (Web application)
- HTML/CSS (UI)
## Approach
- **Data Preprocessing:** Cleaned and processed supply and price data on a monthly basis.
- **Visualization:** Insightful plots help understand seasonal and regional price trends.
- **Model Training:** Used Gradient Boosting Regression (Scikit-learn) to predict price from features like Month, Market Area, Supply (Tonnes), Min/Max Prices (optional).
- **Web Interface:** Built with Flask. Users input month, market, and average tonnes — the app returns a predicted price.
## 📊 Key Insights from the Data
- Most markets receive ~60 tonnes/month - prices crash when supply exceeds this
- Kolar is the largest and most influential market
- Same supply quantities yield different prices across markets (market inefficiency)
- Modal, Max, and Min prices are ~80% correlated
## 📈 Results
- **Training Score**: 99%
- **Test Score**: 98% (when including Max/Min prices - which are strongly correlated to target)
- **Realistic Model (without Max/Min features)**:
- Training Score: 83%
- Test Score: 65%
- RMSE: ₹320
Including external variables like weather, pest activity, or market access could improve real-world performance.
## 📂 Files Overview
| File/Folder | Description |
| ----------------------------------------------- | ------------------------------------------------------------------------------------- |
| `app/monthly_tomato_app.py` | Flask web application |
| `app/templates/monthly_tomato_home.html` | Home page - input form for market, month, and tonnes |
| `app/templates/tomato_result.html` | Result page - displays predicted price |
| `app/static/img/` | Screenshots of the application |
| `data/Price of Tomato Karnataka(2016-2018).csv` | Raw dataset from [Kaggle](https://www.kaggle.com/vinayreddy4034/vegetablepricetomato) |
| `data/monthly_tomato.csv` | Cleaned and preprocessed dataset |
| `models/monthly_gbr_model3.pkl` | Trained Gradient Boosting model |
| `notebooks/tomato2.ipynb` | Data exploration and visualizations |
| `notebooks/monthly_tomato.ipynb` | Model training and evaluation |
| `docs/` | Documentation and analysis files |
| `run.py` | App entry point |
| `requirements.txt` | Python dependencies |
## Learnings
- How to apply machine learning to a real-world problem with practical impact
- The importance of cleaning and preparing data to improve model accuracy
- Some input features (like max/min prices) can give very high accuracy, but don't always reflect real-world conditions
- Building a simple web interface with Flask helps users easily try out our model
## Future Work
- Add real-time data like weather or pest alerts to improve prediction
- Recommend better months or markets for selling produce
- Visualize trends in a more interactive way (dashboards or maps)
- Help farmers estimate profits for different harvest levels
## 📜 Acknowledgements
This project was completed as part of the Project-Based Learning (PBL) contest at **BMS Institute of Technology and Management**, Bengaluru, India.
**Team Members:**
1. Merlyn Mercylona Maki Reddy
2. Aishwarya M