Timon
6bdbea12cc
- Introduced core and optional features for the code improvements merge. - Documented detailed implementation checklist for core features including database migration, model updates, job changes, form modifications, shell script wrappers, and Python package files. - Added optional enhancements such as country-based project organization, download scheduling, project search feature, and harvest prediction setup documentation. - Included testing checklist and post-merge data recreation strategy. |
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| .github | ||
| .vscode | ||
| laravel_app | ||
| old_sh | ||
| python_app | ||
| r_app | ||
| renv | ||
| webapps | ||
| .gitignore | ||
| .renvignore | ||
| .Rprofile | ||
| 10_planet_download.sh | ||
| 20_ci_extraction.sh | ||
| 30_growth_model.sh | ||
| 40_mosaic_creation.sh | ||
| 80_calculate_kpis.sh | ||
| 90_kpi_report.sh | ||
| IMPLEMENTATION_GUIDE.md | ||
| r_app.Rproj | ||
| README.md | ||
| renv.lock | ||
SmartCane - Agricultural Intelligence Platform
A multi-stage satellite imagery processing system for crop health analysis and harvest predictions in sugarcane farming operations.
Overview
SmartCane processes 4-band satellite imagery (RGB + NIR) into actionable crop health insights using a sophisticated data pipeline spanning Python, R, and PHP. The system integrates satellite data from Planet Labs with field-level analysis, automated reporting, and harvest forecasting.
Core Features
- Weekly Satellite Monitoring: 4-band GeoTIFF imagery acquisition and processing
- Chlorophyll Index (CI) Analysis: Crop health assessment with multi-temporal smoothing
- Field-Level KPI Calculation: Uniformity metrics, gap detection, weed risk assessment
- Harvest Forecasting: ML-powered yield predictions with growth trajectory analysis
- Automated Reporting: Weekly Word/HTML reports with field-specific alerts and recommendations
Last Updated: February 2026
Maintained By: Resilience BV
Repository: https://github.com/TimonWeitkamp/smartcane_experimental_area