agriculture
Project Hive 🐝
Agricultural Data Science & Analysis
This repository hosts AI/ML algorithms to support U.S. Agriculture, farms, distributors, restaurants, and consumers. The first analysis focuses on honey production in the United States, leveraging historical datasets to identify production trends, regional variations, and predictive insights.
Value Proposition🪙
Agricultural data is critical for policymakers, farmers, investors, and researchers. By applying AI/ML models to United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) datasets, we can:
- Provide data-driven insights into agricultural production and supply chains.
- Improve forecasting accuracy for commodities like honey, strawberries, and soybeans, reducing uncertainty for producers and distributors.
- Enhance decision-making at the enterprise and policy level with predictive analytics and visualization tools.
- Democratize access to trusted, government-certified agricultural data through advanced machine learning techniques.
Table of Contents📖
- Value Proposition
- Features
- Tech Stack
- Prerequisites
- How to Use
- Enterprise Use Cases
- License
- Contact
- Acknowledgements
Features✨
- Data Ingestion & Cleaning: Automated ETL pipelines.
- Exploratory Data Analysis (EDA): Statistical summaries, trend identification, and visualization of production data.
- Predictive Modeling: Time-series forecasting and regression models for yield estimates.
- Geospatial Analysis: Mapping honey production by state with interactive visuals.
- Scalable Framework: Extendable to other datasets ans scenarios (e.g., sustainabile agriculture, organic farming, fruits, vegetables, livestock, wholesalers, and retailers).
- Reproducibility: Jupyter notebooks and scripts are structured for transparency and replication.
Tech Stack🛠
- Data Sources: USDA API, JSON, CSV datasets
- Languages: Python, Bash
- Libraries: NumPy, Pandas, PyTorch, Scikit-learn, TensorFlow, XGBoost
- DevOps: GitHub
- UX/UI Visualization: CSS3, HTML5, Kotlin, Matplotlib, Plotly, Seaborn, Swift
Prerequisites
- Python 3.10+
- USDA API key
How to Use Demo Model
- Install Jupyter notebook
- Run the program
- Input the year in the console for an estimate of annual honey production (lbs.) per state.
Enterprise Use Cases📈
- Agribusiness & Food Supply Chains. Forecast yields to optimize sourcing, logistics, and inventory planning.
- Policy & Government Agencies. Support decision-making with accurate, transparent agricultural production models.
- Financial Institutions & Investors. Assess agricultural risk, investment opportunities, and market forecasting.
- Sustainability & Environmental Studies. Analyze honeybee population health indicators and pollination-related impacts.
- Education & Research. Provide reproducible, open-access workflows for students and researchers in agricultural economics, data science, and sustainability.
- Supply & Demand. Farms, wholesalers and retailers can forecast capacity to eliminate DOWNTIME and reduce waste.
License⚖️
This project is licensed under a dual license. You are free to use, modify, and distribute this software with attribution for personal or research use, but must open source modifications to the community. Integration into closed source, commercial proprietary systems requires a low cost annual subscription and license fees. See license.txt for details.
Contact📬
Acknowledgements🙏
- The US Department of Agriculture National Agricultural Statistics Service (NASS) for providing the primary data.
- Researchers and policymakers who continue to advance agricultural insights and food security.
- The SWE community for libraries and tools that make reproducible research possible.
- Inspiration from industry leaders in AI/ML models
- I am grateful. Thank you!