Custom Recognition Model Settings

Learn how to configure and optimize AI recognition models, customize settings for specific plant types or regions, and improve recognition accuracy.

Model Type Selection

General Recognition Model

Specialized Domain Models

Medicinal PlantsOrnamental FlowersCropsWild PlantsTropical PlantsSucculents

Regional Settings

Geographic Location Optimization

Seasonal Adjustment

Spring ModeSummer ModeAutumn ModeWinter Mode

Model Training

Custom Dataset

Support users to upload their own plant image datasets for model fine-tuning. Require at least 50 high-quality images per plant species, including samples from different angles, lighting conditions and growth stages.

Incremental Learning

Model can continuously learn and improve from user identification feedback. Support feedback mechanisms such as correctness annotation and error correction. Regularly update model weights to improve recognition accuracy.

Advanced Configuration

Confidence Threshold

Can adjust the confidence threshold of identification results to balance recognition accuracy and coverage. High threshold ensures accurate results but may miss some plants, low threshold increases coverage but may include incorrect results.

Multi-Model Fusion

Ensemble LearningVoting MechanismWeight AllocationResult Fusion

Performance Monitoring

Identification Statistics

Provide detailed model performance statistics, including recognition accuracy, processing speed, resource usage and other metrics. Support analysis by time period, plant type, region and other dimensions.

A/B Testing

Support running multiple model versions simultaneously for comparison testing. Can set traffic allocation ratios, collect user feedback, and select optimal model configuration.

Deployment and Management

Model Version Control

Complete model version management system, supporting version rollback, incremental updates, canary releases and other features. Each version has detailed change records and performance metrics.

Automated Deployment

Support automated model training, testing and deployment processes. Can set periodic retraining schedules to ensure models always maintain the latest state and best performance.