FLATCLASS
- TITLE: FLATfish length and thickness CLASification System by artificial intelligence
- DATE: 2025 - 2026
- FUNDING PROGRAM: IGAPE-IA360: Grants for technological development and innovation through the use of artificial intelligence.
- PARTNERS:
Description
The FLATCLASS project aims to develop an advanced system for the automatic grading of juvenile sole, a key process in modern aquaculture. Traditionally, fish grading has been performed manually, leading to high labor costs, inconsistencies, and stress for the animals. FLATCLASS introduces an innovative solution that combines high-resolution cameras and real-time image processing with artificial intelligence and multiparametric prediction models, enabling precise estimation of fish size and weight.
The system’s main innovation lies in its ability to process multiple channels in parallel, allowing hundreds of fish to be graded per hour with an error margin of ≤5%. A dynamic calibration process ensures that grading thresholds (small, medium, large) are adapted for each session, improving accuracy and consistency. Furthermore, the inclusion of an “error” category makes it possible to detect anomalies or out-of-range measurements, thus strengthening quality control.
Key objectives
Automatic grading with intelligent cameras: analyze fish images in real time to quickly and accurately determine size and weight.
Accurate weight estimation: apply advanced mathematical models to calculate fish weight with a very low error margin.
Dynamic adjustment to each batch: automatically adapt grading thresholds (small, medium, large) to the actual characteristics of each group.
Anomaly detection: identify out-of-range or inconsistent measurements to strengthen quality control.
Large-scale processing: grade hundreds of fish per hour across multiple parallel channels, ensuring speed and efficiency.
Data management and traceability: store all information in an organized and accessible way to facilitate long-term monitoring of production.
Advanced on-site technology: perform real-time processing directly on local high-performance devices (Edge Computing with GPUs), reducing latency.
User-friendly interface: provide an intuitive control system that enables operators to easily manage and monitor the grading process without technical expertise.
Feed efficiently, produce more, and reduce costs.
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