https://github.com/ashishpatel8736/soybean-dss
The Soybean Decision Support System (DSS) is a machine learning-powered web application that predicts soybean yield and protein content based on key agricultural parameters. Farmers, researchers, and agronomists can use these insights to optimize productivity and enhance crop quality.
https://github.com/ashishpatel8736/soybean-dss
algorithms app machine-learning python streamlit
Last synced: about 1 month ago
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The Soybean Decision Support System (DSS) is a machine learning-powered web application that predicts soybean yield and protein content based on key agricultural parameters. Farmers, researchers, and agronomists can use these insights to optimize productivity and enhance crop quality.
- Host: GitHub
- URL: https://github.com/ashishpatel8736/soybean-dss
- Owner: ashishpatel8736
- License: mit
- Created: 2025-03-01T14:48:19.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-01T15:00:32.000Z (over 1 year ago)
- Last Synced: 2025-03-01T16:18:49.388Z (over 1 year ago)
- Topics: algorithms, app, machine-learning, python, streamlit
- Language: Python
- Homepage: https://soybean-dss.streamlit.app/
- Size: 752 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Soybean Decision Support System (DSS)
## 🌱 Overview
The **Soybean Decision Support System (DSS)** is a **machine learning-powered** web application that predicts soybean **yield** and **protein content** based on key agricultural parameters. Farmers, researchers, and agronomists can use these insights to **optimize productivity** and **enhance crop quality**.
## ✨ Features
✅ **Interactive UI** – Select input presets or manually adjust parameters.
✅ **Real-time Predictions** – Get instant soybean yield & protein estimates.
✅ **Easy-to-Read Insights** – Understand predictions with helpful **tips & visualizations**.
✅ **Multiple Presets** – Test different conditions (High/Low Yield & Protein).
✅ **Robust Model** – Trained on real-world **agricultural data** for accuracy.
## 📂 Project Structure
```
soybean-dss/
├── data/
│ └── soybean_data.csv # Dataset used for training
├── model/
├── soybean_model.pkl # Trained ML model
├── scaler.pkl
└── train_model.py # Model training script # Scaler for feature normalization
├── logos/
│ └── train_model.py.png
├── app.py # Streamlit web application
├── requirements.txt # Dependencies
└── LICENSE # License File
└── README.md # Project documentation
```
## 🛠 Installation
### 1️⃣ Clone the Repository
```bash
git clone https://github.com/ashishpatel8736/soybean-dss.git
cd soybean-dss
```
### 2️⃣ Create & Activate Virtual Environment
```bash
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
```
### 3️⃣ Install Dependencies
```bash
pip install -r requirements.txt
```
### 4️⃣ Run the App
```bash
streamlit run app.py
```
## 📊 Interpretation Guide
| Yield Prediction | Meaning |
|-----------------|---------|
| **High Yield** (> 6000 kg/ha) | Excellent productivity 🚀 |
| **Average Yield** (3000 - 6000 kg/ha) | Good, but may need adjustments 📊 |
| **Low Yield** (< 3000 kg/ha) | Requires significant improvement ❗ |
| Protein Content Prediction | Meaning |
|---------------------------|---------|
| **High Protein** (< 0.7) | Excellent nutritional quality 💪 |
| **Moderate Protein** (0.7 - 1.2) | Acceptable balance ⚖️ |
| **Low Protein** (> 1.2) | Needs improvement! Consider fertilizers 🌿 |
## 👤 Author
**Ashish Patel**
[](https://github.com/ashishpatel8736) | [](https://www.linkedin.com/in/ashishpatel8736)
## 📢 Contributing
💡 Found a bug? Have an idea? Feel free to open an issue or submit a pull request!
## 📜 License
Distributed under the MIT License. See `LICENSE` for details.
---
🚀 *Happy Farming & Smart Decision-Making!* 🌾