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.

VinQ ([https://vin-q.com/](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. 


