Spade Project
Multi-purpose physical-cyber agri-forest drones ecosystem for governance and environmental observation
OPEN CALL #2
Call for Applicants
PRE-REGISTRATION UNTIL
3 MARCH 2025
Applicants
What we are looking for?
Open Call#2 seeks 9 talented innovators to advance research in agriculture, livestock, and forestry applications, with a total budget of €525,000, funding 8 projects at €60,000 each and 1 project at €45,000, for a duration of six months.
Eligible projects must align with one of three SPADE Case Study groups:
Open-Field Case Study (Spain)
Focusing on drones in farming to streamline tasks and provide insights for better crop and field management.
Forestry Case Study (Norway):
Exploring drone swarms, tethered drones for harvesters, and heavy-lift drones.
Livestock Case Study (Greece):
Enhancing sheep breeding through grazing and health monitoring with multi-purpose UAVs.
Universal Consideration Across Case Study:
Enhancing efficiency, monitoring environmental conditions, and improving productivity through the integration of UAV technology in the open-field crops, forestry and livestock management.
This initiative is part of SPADE’s mission to build a thriving ecosystem for innovation and digital transformation in agriculture, forestry and livestock.
Challenges
UCU-CH1: Swarm Communication and Navigation
Challenge: To develop a communication and navigation system for drone swarms to map areas using LiDAR. A central computer will process sensor data into a detailed map while coordinating swarm navigation to ensure full coverage.
UCU-CH2: Open-Source Tilted Rotor Drone System
Challenge: To develop an open-source omnidirectional navigation system using a tilted rotor drone design. The proposal should include hardware development for tilted rotors and the creation of a vectored thrust controller as an extension to the PX4 flight controller. This controller will manage rotor tilting angles and speeds, with the software operating on the drone’s onboard computing unit.
UCU-CH3: Aerial Mapping for SPADE Platform
Challenge: To develop and deploy an AWS-based system for uploading raw images, processing them into orthomosaics, and integrating with a third-party microservice for data storage and further analysis. The system will include APIs for image upload, processing options, and data download, supporting radiometric calibration images, multiple data outputs, and connection with an FMIS.
CS1-CH1: ML for Disease Detection in Mediterranean crops
Challenge: To explore innovative applications of machine learning (ML) for disease detection on plants and crops, primarily focusing on orange trees and potatoes. Analyse large datasets to identify patterns, predict outbreaks, and enhance diagnostic accuracy to improve crop health and agricultural sustainability.
CS1-CH2: Open Field Data Collection and Annotation
Challenge: To acquire and structure open-field crop datasets to train cloud-based ML models for at least two focused applications, requiring access to multiple fields for data collection. As part of the Open Field Case in the SPADE project, it supports the creation of comprehensive segmented datasets of georeferenced aerial images captured by UAVs equipped with RGB, thermal, multispectral, or hyperspectral cameras.
CS2-CH1: Below-Canopy Forest Data Collection and Annotation
Challenge: To support the collection and annotation of a comprehensive European below-canopy forestry dataset. Include annotated images and point clouds to facilitate segmentation and classification of below-canopy forest environments.
CS2-CH2: Ultralight Self-Levelling Landing Gear for Quadrotor Drones
Challenge: To design adaptive, lightweight landing gear solutions for quadrotor drones. Ensure safe take-off and landing on uneven terrain with minimal added weight.
CS3-CH1: Virtual Fencing Application for Livestock Grazing Management
Challenge: To develop a virtual fence thresholding application for targeted grazing management. Utilize the SPADE Livestock Platform (SLP), Parrot Anafi USA UAV, and animal collar transceivers to monitor livestock, collect relevant data via SLP, and validate the solution in real-world scenarios by securing access to a livestock farm.
CS3-CH2: Acquiring and structuring livestock open field animal wellbeing/healthcare datasets for training ML models
Challenge: To acquire and structure livestock open-field health and welfare datasets to train cloud-based machine learning models. Achieve over 80% accuracy in detecting at least one specific animal vector-disease variation. Secure access to a livestock farm for data collection and validation.
Selection Process
- Application: Applicants must create a profile on the SPLORO platform, complete the registration form, and upload their proposals with the defined template and required documents within the application period.
- Evaluation: This step includes three parts (Eligibility Check: to ensure the applicant meets all required criteria, Alignment Check: to verify the proposal aligns with the objectives of the Open Call, and External Evaluation: Proposals are reviewed and scored by a panel of experts based on predefined criteria)
- Legal Validation and Sub-grant Agreement: Selected applicants undergo legal validation and sign a sub-grant agreement to formalize their participation.
- Result Announcement: The results are publicly announced after signing the sub-grant agreement
- Implementation Period: Funded projects begin their implementation phase, adhering to agreed-upon timelines and milestones.
Support
Guidelines, FAQs and other supporting documents will be available soon. For your inquiry, send an email to Click Here
📄 Guidelines
📄 Support Documents
FAQs
📄 Guidelines
Discover collaborative opportunities for academia, NGOs, SMEs, and startups in pioneering drone technology solutions. Apply now to contribute to SPADE’s mission of innovation and societal benefit.
📄 Support Documents
Access supplementary documents for comprehensive guidance on SPADE’s collaborative approach across academia, NGOs, SMEs, and startups in advancing drone technology solutions:
• Technical Annex • Application Form• Grant Agreement• SME_Declaration• Ethical Guidelines
FAQs
Uncover answers to common queries regarding SPADE’s collaborative framework, application process, and project expectations:
How to Apply
Express your interest and stay informed about SPADE Open Call #2 by pre-registering from 2 January 2025 to 3 March 2025. Gain early access to updates, important deadlines, and tailored resources to support your proposal submission. This is your chance to join an innovative initiative advancing UAV technology for sustainable agriculture, forestry, and livestock management.
Don’t miss out—click pre-register button now and take the first step toward shaping transformative solutions for a sustainable future!
Results
The outstanding projects will be announced in mid-June after the sub-grantee agreement is signed.