Vin-Q aims to initiate a global transition to regenerative agriculture through the collective action of distributed research. By adopting a data-centric approach, Vin-Q seeks to transform farming practices through data-guided decisions and knowledge sharing.

To achieve this, Vin-Q is developing data-centric tools within the platform that not only collects data but also processes it using various disease models and mitigation scenarios. One of the solutions is the development of AI agents, trained on specific field data and historical information that allow fully personalization by the farmer to assist in decision-making by linking data sources of the field and providing predictions.

These tools link existing scientific literature with real-time IOT sensors, soil health data and historical practice records, helping farmers to test and implement regenerative agriculture techniques. They aim at visualization and highlight the need for changes in agricultural practices also suggesting the best practices. Establishing predictive and personalized models for each farm and prediction of diseases aims to reduce treatments in organic farms and scientifically demonstrate the potential of regenerative agriculture with visual and simple platform.

On other side, the network of sensors and experiences represent a distributed and decentralized laboratory of decentralized science that provide evidence of best practices in the transition towards personalized regenerative practices fully controlled by farmers.

By integrating a data platform with machine learning algorithms and visualization tools, Vin-Q can not only improve decision-making processes but also predict future vineyard performance and optimize management strategies proactively.

1. Enable Rapid Adaptation of regenerative practices through Iterative learning from real-time data in the field of farmers ensures that the platform can quickly adapt to changing conditions.
2. Encourage Community Innovation: Users can share and valorize personalized strategies, fostering swarm intelligence and collaborative problem-solving.
3. Reducing Centralized Risks: Distributing AI capabilities across a network of autonomous agents reduces the risks associated with centralized systems and "expert" advices.
4. De-risk the transition to regenerative agriculture through sharing knowledge from successful applications.
   5.** Provide a distributed and decentralized research** space to test new methods, ideas in real environment with the participation of farmers.

Climate change is prompting farmers to seek out more research and technology-intensive solutions to mitigate the increasing challenges associated with more severe weather conditions.

Critical challenges are related with

• increased risks of diseases affecting vineyards due to changing climatic conditions,
• desertification,
• fragmented knowledge on regenerative practices,
• the need for adaptation to new environmental circumstances,
• severe conditions associated with climate change.

VinQ (https://vin-q.com/) platform is an example of distributed research (DeSci) and new type of collaboration based on sharing reliable data. The farmers collect data individually in their fields, establishing their priorities and objectives.

The data can be categorized into two types: real, verifiable data reflecting the actual state (ground truth) such as microbiology, climate, soil quality, diseases, and production, which can be confirmed through fact-checking and represents a point in a parameter space. The other type of data pertains to the management style, which includes actions that affect points in a parameter space. Any treatments or fieldwork carried out to mitigate problems or improve conditions reflect the personalized style of the individual. By utilizing AI to connect these two types of data, a governance system is established, where individuals autonomously explore parameter space while AI is trained on real-world consequences.

Want to grow through just grant funding

Long term: Global, Actual: Spain

1000$