Evaluating the Model & Making Predictions
Learn how to evaluate your trained CNN model and use it to make accurate predictions on new plant images.
Why Evaluate Your Model?
After training your CNN model, evaluation is a critical step to understand how well it will perform on new, unseen plant images. Proper evaluation helps you:
- Determine if your model is ready for deployment
- Identify specific disease classes that may need more training data
- Understand the model's strengths and limitations
- Compare different model architectures or hyperparameter settings
- Build confidence in your model's predictions
Key Insight
Good performance on training data but poor performance on test data indicates overfitting! Always evaluate on data your model hasn't seen during training.
Evaluation Workflow
Evaluation Process
Load Test Data
Load unseen test images that the model has never encountered during training.
Preprocess Images
Forward Pass
Calculate Metrics
Analyze Results
From Evaluation to Prediction
Once your model is properly evaluated and meets your performance criteria, it can be deployed to make predictions on new plant images. The prediction process involves:
Prepare new images using the same preprocessing steps applied during training (resizing, normalization, etc.)
Pass the preprocessed image through your trained CNN to get raw prediction scores for each disease class
Convert prediction scores to probabilities and determine the most likely disease class
