Objectives → Results → Impacts with KPIs, Narratives, WP alignment, and Quality Check
Rationale: Agriculture faces the dual challenge of feeding a growing population while reducing environmental impacts. Current practices often overuse water, fertilizers, and pesticides, leading to resource inefficiencies and environmental harm. Drone technology with AI analytics can enable precision agriculture, ensuring targeted resource use, higher yields, and reduced ecological footprint.
SMART Objectives:
| Objective (SMART) | Expected Result (Output) | Expected Impact (Effect) | EU Priority Addressed |
|---|---|---|---|
| Develop AI-enabled drone prototype (M12) | Functional prototype with validated imaging/analytics | Proof of sustainable agri-drone technology | Digital & Green Transition |
| Integrate precision analytics to reduce input use (30% in 3 years) | Farm decision support integrated with drones | Lower water, fertilizer, pesticide usage | Farm-to-Fork, Climate Neutrality |
| Demonstrate yield improvements in pilot farms (M24) | Field demo results, comparative yield data | Productivity gains with lower input cost | Food Security, Rural Competitiveness |
| Establish open API connectors (M18) | API documentation and connectors | Ecosystem interoperability | Data Spaces, Open Innovation |
| Scale adoption with cooperatives and leasing models (M36) | Adoption roadmap & service model | Wider farmer uptake of sustainable tech | Digital Economy, Inclusive Growth |
| Impact Level | Change Description | KPIs (baseline → target) | Measurement Methods | Target Stakeholders | Adoption Pathway | Risks & Mitigation |
|---|---|---|---|---|---|---|
| Farm input efficiency | Reduced use of water/fertilizer/pesticides | Water use: 3,000 m³/ha → 2,400 m³/ha (-20%) by M36; Fertiliser N: 180 kg/ha → 135 kg/ha (-25%) by M36; Pesticide TFI: 1.0 → 0.7 (-30%) by M36 (aligned with Farm-to-Fork 50% pesticide reduction by 2030) | Farm input logs, drone analytics | Farmers, cooperatives | Field demos, ROI calculators | Weather variability → multi-season pilots |
| Crop productivity | Increased yields per hectare | Yield: 5 t/ha → 5.75 t/ha (+15%) by M24 | Yield measurements, satellite/drone data | Farmers, agri-businesses | Farmer schools, insurer tie-ins | Adoption resistance → peer champions |
| Economic viability | Higher net margin per hectare | Net margin: €1,200/ha → €1,380/ha (+15%) within 3 seasons | Farm financial reports | Farmers, SMEs | Leasing / Drones-as-a-Service | Capital cost → leasing/DaaS |
| Ecosystem interoperability | Open API integration with OEM machinery | # of connectors: baseline 0 → 3 by M18 | API usage stats, interoperability tests | OEMs, tech SMEs | Open APIs, documentation | Interoperability gaps → open API standards |
| Adoption scale | Uptake of drone service models | Farms adopting: baseline 0 → 250 farms by M36; Hectares covered: baseline 0 → 50,000 ha by M36 (policy-aligned) | Adoption surveys, service provider reports | Farmers, cooperatives, SMEs | Cooperative partnerships, leasing/DaaS | Uptake risk → farmer champions, insurer tie-ins |
| Work Package | Main Deliverables | Contribution to Objectives & Results | Link to Impacts | Measurement Contribution |
|---|---|---|---|---|
| WP1: Prototype Development | Drone hardware/software prototype | Supports Obj.1 | Proof of tech | Prototype performance reports |
| WP2: Integration & Analytics | AI algorithms, farm system connectors | Supports Obj.2, Obj.4 | Input efficiency, interoperability | Analytics validation reports |
| WP3: Demonstration & Pilots | Pilot farm trials, yield data | Supports Obj.3 | Productivity, efficiency | Field measurements, KPIs |
| WP4: Adoption & Exploitation | Cooperative partnerships, leasing model | Supports Obj.5 | Economic viability, uptake | Adoption surveys, ROI calculators |
The project develops and validates a sustainable agricultural drone system integrating multispectral imaging and AI-driven analytics for precision farming. By embedding drones into farm management systems, the project reduces input use while boosting productivity. Field trials will demonstrate real-world benefits, while open API connectors ensure interoperability. The adoption model leverages farmer cooperatives and leasing to ensure affordability and wide uptake.
If drones reduce input use by 20–30% within three years, this directly supports the EU Farm-to-Fork Strategy, which targets a 50% reduction in pesticide use by 2030 and a 20% reduction in fertilizer use. Demonstrated yield gains (+15% by M24) contribute to EU food security and competitiveness in rural areas, showing that sustainability and productivity can advance together.
The adoption pathway—250 farms and 50,000 hectares by M36—contributes to the CAP eco-scheme pilots and aligns with the Green Deal goal of 25% organic farmland by 2030, since precision input reduction is a recognised enabler for organic transition.
Open APIs strengthen interoperability in line with the EU Data Spaces strategy, ensuring inclusiveness and enabling SME-driven innovation. By demonstrating carbon savings (0.5 tCO₂eq/ha avoided by M36), the project also positions itself within the EU Climate Law net-zero trajectory for 2050.
Thus, the project does not just deliver technical gains but provides a direct policy fit, ensuring measurable contribution to EU Green Deal, Farm-to-Fork, and Climate objectives.
By 2030, the system can be scaled across EU farms, replicated in global markets, and integrated with carbon accounting for sustainable finance. It aligns with SDGs 2 (Zero Hunger), 12 (Responsible Consumption), and 13 (Climate Action). Sustainability is ensured via service-based models, reducing upfront costs and promoting continuous innovation.
“By 2030, our AI-powered agricultural drone system will scale across Europe, enabling farmers to produce more with less, accelerating the transition to climate-neutral, resource-efficient, and resilient food systems in line with the SDGs on Zero Hunger, Responsible Consumption, and Climate Action.”
Use this list to produce slide-ready charts (baseline vs. target, adoption growth, policy contribution).