Machine Learning Data Structure for Medical Device Reference Retrieval

The patent segments the identification system into multiple specialized models (e.g., crop type identification, harvest time prediction, quality assessment) that can be independently trained and updated. This allows updating only specific segments when new data is available, rather than retraining the entire system, thereby reducing updating time while maintaining identification accuracy.

The patent implements preliminary data collection and model training for various crop types and conditions before actual deployment. Pre-trained models and databases are prepared in advance, allowing the system to quickly adapt to new crops by leveraging existing preliminary work rather than starting from scratch, thus reducing updating time.

The patent divides comprehensive image analysis into multiple specialized processing stages (e.g., color analysis, shape analysis, texture analysis) that can be performed in parallel. Each stage focuses on specific characteristics, improving overall determination accuracy while reducing total processing time through concurrent execution.

The patent implements a multi-level analysis approach where basic crop identification is performed first using simplified criteria, and only then are more comprehensive analyses applied when needed. This partial action approach maintains accuracy for common cases while reducing processing time by avoiding excessive analysis for all images.