Mul Biotech Farms · 2026
A resource-intelligence dashboard covering every Indian state — crop calendars, water footprints, irrigation risk, and structured field research, all built on one clean data model.
The cropping calendar this dataset is built around
Select a report below and download the .pbix file — open it in Power BI Desktop to explore the full interactive dossier.
28 states · Irrigation risk · Water footprint · Crop season calendar
↓ Download .pbixA star schema. Every fact table joins back to dim_State on the state name.
All relationships: dim_State[State] → fact tables · one-to-many
Seven stages take multi-contributor research and scraped market data into one normalized workbook.
Selenium headless Chrome pulls commodity prices from 30 state portals, clicking through pagination automatically.
Scraped CSV uploads to Google Drive via a service account. GitHub Actions runs it on a daily schedule.
All 29 research sheets parsed into six structured tables, cached with pickle to skip repeat work.
87 issues found: packed cells, LaTeX artifacts, junk placeholders, 65% blank seasons, missing states.
An 80+ crop knowledge base classifies every blank season into Kharif, Rabi, Zaid, or Perennial.
ICAR-based lookup tables fill 49 blank footprints and 70 blank irrigation ranks.
Risks trimmed to 3 bullets, SWOT to 8–12 word headlines, advantages to under 10 words.
Six Excel Table objects, zero blanks, zero junk — ready for Power BI.
Dashboards, datasets, scripts, and docs all live in the repo.
Crop water & resource deep dive. Page 1: state dossier. Page 2: crop economics & water sustainability.
Executive commodity analysis and state-level market intelligence report.
Commodity price intelligence and mandi market analytics across states.
The normalized star-schema workbook. Six Excel Table objects, zero blanks across all tables.
The original 29-sheet research workbook from all four contributors, before cleaning.
Selenium scraper plus Google Drive uploader. Pulls prices from 30 portals and syncs to the cloud.
State research was split across the team; each of the three dashboards was built by a different contributor.
Clone, install, scrape, and open the dashboards.
git clone https://github.com/soumodip18/IRBAS-Agricultural-Intelligence-Dashboard.git cd Automated-Agricultural-Intelligence-Pipeline
pip install selenium webdriver-manager pandas openpyxl \
google-auth google-api-python-clientCreate a Google Cloud service account, download its JSON key, and store it as a GitHub Actions secret named GCP_CREDENTIALS. The scraper reads it at runtime.
# scrapes all 30 states, uploads the master CSV to Drive python scripts/cloud_pipeline.py
Download a .pbix from dashboard/, open it in Power BI Desktop, then point its source to data/Custom_PowerBI_Agriculture_Model.xlsx under Transform Data → Data Source Settings.
This project is open-access for researchers, policymakers, and agricultural professionals. Please credit the team and organisation when sharing or referencing this work.
This dashboard and the underlying datasets were developed as part of the Strategic Agriculture Intelligence Framework under Mul Biotech Farms. Data is sourced from official government publications, peer-reviewed research, and verified agricultural databases. If you share, cite, or build upon this work, please credit Mul Biotech Farms and the contributing team. For research collaborations or data access, reach out via the GitHub repository.
This project was developed under Mul Biotech Farms by the contributing research team. By accessing, downloading, or sharing this work, you agree to the following terms:
· Credit Mul Biotech Farms and the team in any publication, post, or presentationUnauthorized use, plagiarism, or failure to provide explicit attribution constitutes a violation of the usage terms and will result in intellectual property reporting and action.