cropforge 0.5.0

Open-source, code-first virtual farm runtime for agricultural researchers.

CropForge

Open-source, code-first virtual farm runtime for agricultural researchers.

CropForge lets you define a crop simulation entirely in Python. You write the model equations; CropForge handles time-stepping, spatial state management, logging, and visual playback.

pip install cropforge

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What's New in v0.5.0

White Minimal Scientific Dashboard

A complete rework of the visualisation layer built on Material Design:

• Sleek, clean white minimal theme with refined borders and ample whitespace.
• Persistent Farm Inspector (always visible, no click-to-reveal).
• Left sidebar with all controls; right sidebar with live charts.
• Animated preloader with per-field progress tracking.
• All panels renamed to researcher-facing terminology.

Official Crop Plugins — StandardWheat & StandardMaize

First-party, PRD-verified crop plugins distributed as cropforge.plugins:

from cropforge.plugins import StandardWheat, StandardMaize

field.use_plugin(StandardWheat)    # CERES-Wheat phenology + RUE biomass + grain fill
field.use_plugin(StandardMaize)    # C4 parameters + root impedance + water stress

• StandardWheat: 6-stage thermal-time phenology, RUE biomass, grain-fill partitioning into plant.custom['grain_biomass_g'].
• StandardMaize: C4 photosynthesis parameters, hard-pan root clamping, water-stress mortality.
• Both plugins are fully isolated per field — no cross-talk when running dual-plot comparisons.

Advanced Physics Engines

Two new opt-in physics engines, mathematically verified against PRD Crucible criteria:

Beer-Lambert Radiation Interception

farm.use_physics(radiation=True, k_extinction=0.45)
# Writes plant.custom['intercepted_par_mj'] for every plant every day

Implements: PAR_int = solar_rad × 0.5 × (1 − e^(−k × LAI)) Crucible verified: LAI=3.0, k=0.45, rad=15 MJ → 5.5557 MJ ±0.001.

Wind-driven Anisotropic Disease Spread

farm.use_physics(
    disease=True,
    disease_foci=[(15, 15)],          # Initial outbreak coordinates
    disease_wind_direction_deg=270.0,  # From West → spreads East
    disease_spread_rate=0.20,
    disease_anisotropy=0.80,
)

A spatially explicit SIR grid model where infection probability is heavily weighted by wind direction. Crucible verified: eastern half has 2.5× more infections than western half when wind blows East.

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Quick Start

from cropforge import Farm, Field, Crop, Soil, Weather
from cropforge.plugins import StandardWheat

farm  = Farm(name="MyFarm", location=(28.6, 77.2))
field = Field(name="Plot A", rows=20, cols=30, area_ha=2.4)
field.set_crop(Crop(species="wheat"))
field.set_weather(Weather.from_csv("data/weather.csv", ...))
field.set_soil(Soil.from_csv("data/soil.csv", apply="uniform"))
farm.add_field(field)

field.use_plugin(StandardWheat)
farm.use_physics(radiation=True, disease=True, disease_wind_direction_deg=270.0)

farm.run(days=90)

See examples/wheat_basic_v2.py, examples/maize_dual_plot_v2.py, and examples/disease_outbreak_trial.py for complete working scripts.

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What's New in v0.4.0

• Plugin Ecosystem: Extensible architecture for third-party crop models via PyPI.
• Multi-Season Rotations: Preserve soil state between consecutive runs.
• Compare Dashboard: Compare multiple farm configs and export to CSV.
• Spatial Hydrology: D8 lateral surface water routing across gridded fields.

What's New in v0.2.0

• Opt-In Physics: FAO-56 Penman-Monteith ET0 and Root Impedance models.
• Multi-Field Dashboard: Field Selector, comparative time-series, GxE analysis.

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Documentation

Full documentation at cropforge.readthedocs.io.

Licence

MIT — Saswat Sundar Rath, ICAR-IARI Jharkhand, 2026