AI-based Decision Support for Risk-Aware Agrobiodiversity and Diversified Farming Systems
Crop diversification practices — longer rotations, legumes, intercropping, cover crops, species-rich leys and reduced-input management — enhance soil biological functions, reduce reliance on synthetic fertilisers and pesticides, and strengthen resilience to climatic stress.
Yet uptake on commercial holdings remains limited, not because farmers are unaware of the agronomic benefits, but because they must weigh uncertain agronomic, economic and market outcomes under variable local conditions: how much income might be lost during the transition years, what is the probability of a bad year, when does the change break even, will existing machinery work, and is there a viable buyer for the diversified crop.
AgroEcoWatch-AI starts from this diagnosis: rather than merely demonstrating once more that agrobiodiversity is beneficial, it makes the risk of adopting it quantifiable, region-specific and legible to the farmer who carries that risk.
An uncertainty-aware AI decision-support system that quantifies the benefits, transition costs and risks of diversified farming systems at parcel and farm level.
Unlike conventional advisory tools that return deterministic prescriptions without confidence bounds, AgroEcoWatch-AI links harmonised on-farm observation, remote sensing and bio-economic analysis into a risk-aware decision-support system validated to TRL 4.
Predictive models return calibrated prediction intervals for yield stability, soil trajectory, water stress and gross margin. Where a query falls outside the validated domain of soil, crop, region or season, the system explicitly states its coverage limits and declines unsupported extrapolation.
The simulation engine evaluates alternative rotations and crop combinations against a holding's own history, propagating uncertainty into gross-margin distributions while accounting for transition costs, break-even timing, labour peaks, machinery constraints and real market-access conditions.
Fuses parcel-aligned Copernicus satellite series (Sentinel-1 and Sentinel-2), UAV reference imagery, proximal and IoT field sensors, soil-health and microbiome indicators, and a provenance-tiered participatory intake of parcel land-rating (bonitation) records.
Delivers drought and crop-stress alerts from combined Earth-observation and IoT anomaly signals, a farmer-facing advisory interface where recommendations are traceable to supporting field evidence, and a public environmental-intelligence portal (the AgroEcoWatch Observatory).
The platform architecture connects seven stages, from multi-modal parcel observation to evidence-backed recommendations and iterative seasonal learning.
Copernicus satellites, drones, field IoT sensors, soil microbiome, weather and farm accounts
Parcel alignment and assembly of current field and holding state on a FAIR common data model
Calibrated forecasts for yield stability, water stress, pest pressure, soil trajectory and margin
Counterfactual testing of crop rotations, intercrops, input regimes, climate and market scenarios
Multi-objective balancing of profitability, climate resilience, biodiversity and farm feasibility
Evidence-traceable recommendations with explicit uncertainty intervals and coverage bounds
Human-in-the-loop validation, on-farm implementation and continual seasonal model updating
Observed harvests, soil trajectories and farmer choices feed back into Step 01 for the next season
Within the AgroEcoWatch-AI initiative, Mireșu Mare Agricol Smart Cluster co-develops predictive AI models and connects applied research, shared AI compute infrastructure and commercial farming practice in the Maramureș region:
AgroEcoWatch-AI is a European project proposal submitted under Horizon Europe – Cluster 6 (Food, Bioeconomy, Natural Resources, Agriculture and Environment). Full details regarding the international consortium and implementation schedule will be published following the completion of the evaluation process.
