Synthetic Agrifood Data Generation and Augmentation

The customer will receive: Access to GPU environments optimised for generative AI and 3D simulation engines. The output dataset resulting from the generation or augmentation process. Rapid data processing and transformation capabilities. With this, the service solves: The cold-start problem in AI: allows startups to begin training models before they have collected massive amounts of physical data. Class imbalance in datasets: helps users augment existing datasets with rare occurrences (e.g., severe weather impacts, uncommon pests) to make AI systems more robust and reliable. Data acquisition costs: significantly reduces the time and expense associated with manual data collection and annotation in the field.

The client is provided with direct access to a dedicated computational node configured for data generation, or they can request a "generation-as-a-service" pipeline where AgrospAI engineers assist in running the specific synthetic data protocols. The resulting datasets are securely transferred to the client or deposited directly into a data room for subsequent AI training.

The generation parameters are highly customisable, particularly when AgrospAI engineers are directly involved in the generation pipeline (generation-as-a-service modality). In this case, parameters such as crop varieties, synthetic pest mapping, weather condition simulation, and specific sensor modality outputs (e.g., RGB, thermal, multispectral) can be configured. When the client deploys their own generative models on the infrastructure, the customisation is determined by the client's own software within the allocated computational environment.