Image
Overview
Provide support for improving existing models/algorithms/software through on-field data acquisition and online learning, e.g., for crop protection against agents and pests. Customers equipped with AI-based solutions will receive support and knowledge transfer enabling solution improvement. The customer will receive access to the physical infrastructure for iterative solution tests and improvements.
More about the service
With this service we can address specific needs for the improvement of existing AI solutions arising in application to the demanding real-world problems of specific crop protection (e.g., assessment of vegetation process or potential pest infection, application of adequate preventive measures) through acquisition of domain-specific datasets and using them to train or fine-tune existing AI models, as well as providing access to the infrastructure allowing us to test the specific developed solutions.
The duration and details of the service delivery can be adjusted according to the characteristics of a problem at hand and specific customer needs. The service can be delivered at any time suitable in the context of the processes involved, e.g., vegetation periods. It might be executed remotely, when involving only improvement of AI models not requiring physical work, or on-site when involving on-field data acquisition or testing using physical infrastructure. The customer will receive the improved solution together with detailed documentation and a report. The customer needs to provide the solution to be improved.
The specific range and details of possible actions performed within this service can be customised flexibly depending on the technology involved and according to the customer needs.
Location
At user's premises
Poland
Remote
Type of Sector
Arable farming
Food processing
Greenhouse
Horticulture
Tree Crops
Viticulture
Type of service
AI model training
Collection of test data
Data augmentation
Performance evaluation
Provision of datasets
Accepted type of products
Software or AI model
