Farm Management Software

Part of the worlds-biggest-software-project initiative.

An open, AI-native farm management platform for field mapping, crop planning, input tracking, and harvest records — without single-vendor lock-in.

Farm Management Software is a candidate project to build an interoperable, AI-augmented farm management information system (FMIS) for row-crop, horticultural, and mixed operations. It targets growers, agronomists, and farm management companies who need field-level planning, compliance, and profitability analysis without being locked into a single agrochemical or equipment vendor's ecosystem.

Why Farm Management Software?

Incumbents like Climate FieldView and Granular are owned by Bayer and Corteva, creating ecosystem lock-in concerns for growers wary of tying farm data to agrochemical suppliers.
Hardware-coupled platforms (Trimble Ag, Agjunction/Hexagon Ag) deliver deep integration only within their own equipment lines, leaving mixed-fleet farms underserved.
Enterprise platforms (Granular, Conservis, AgriERP) use custom pricing and are too heavyweight and expensive for small and mid-sized operators.
Low-cost tools (FarmLogs, FarmKeep) are accessible but lack agronomic depth, machinery integration, and enterprise analytics.
Farmers face growing interoperability pressure as carbon credits, sustainability reporting, and retailer traceability requirements demand portable, standards-based farm data.

Key Features

Core Field & Crop Management (MVP)

Field mapping and boundary management
Crop planning and input tracking
Equipment and machinery management
Weather integration
Financial reporting and P&L analysis
Compliance tracking

Agronomic Depth (v1.1)

Pest and disease monitoring library
Yield prediction
Equipment telematics integration
Precision agriculture recommendations
Budgeting and cash flow forecasting
Multi-party agronomic collaboration between growers, agronomists, and retailers

Advanced & Sustainability (Backlog)

Satellite imagery and field monitoring
Sustainability and carbon reporting
Supply chain management
Market price integration
Predictive equipment maintenance
AI-driven input and rotation optimisation

AI-Native Advantage

AI-driven crop planning combines field history, soil type, weather forecasts, commodity futures, and input costs to recommend optimal rotations and planting populations at the field level. Equipment telemetry is converted into agronomic records automatically, keeping compliance documentation current without manual entry. A natural-language farm advisor answers questions about pest identification, treatment options, and application timing using the farm's own data, while predictive mid-season yield maps and input-optimisation models help reduce fertiliser and chemical use without sacrificing output.

Tech Stack & Deployment

The project is expected to align with established agricultural data standards: ADAPT for cross-platform farm data interoperability, ISO 11783 (ISOBUS) for machinery and implement telemetry, the FAO FMIS Data Dictionary as a reference data model, AgGateway AGIIS for product and supply chain identification, and GS1 for input and commodity traceability. Deployment modes and SDK details remain to be specified.

Market Context

The global farm management software market was estimated at USD 4.18 billion in 2024 and is projected to reach USD 10.58 billion by 2030 at a CAGR of 17.3% (Grand View Research, 2024). Pricing ranges from free tiers (FarmLogs, FarmKeep) through mid-market subscriptions at USD 49–200/month (Agrivi) to enterprise custom pricing tied to acres or revenue (Granular, Conservis, Trimble). Primary buyers include large-scale row crop producers, specialist horticultural growers, agronomists, farm management companies, and agri-lenders requiring operational reporting.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.