Model Accuracy

The Crop Classification model delivered by Pixxel Aurora provides reliable identification of crop types across agricultural landscapes using satellite multispectral data. The model’s performance is validated through two complementary strategies: supervised classification, which leverages user-provided information about known crops, and unsupervised clustering, which groups farms based on similarities in vegetation patterns without prior labels.

• Supervised Model Accuracy : This approach generally achieves classification accuracies in the range of 70% to 80% when compared against labeled reference farms. It is especially effective at differentiating crops with distinct growth signatures—achieving accuracies exceeding 90% for certain crops such as maize and potato.
• Unsupervised Model Accuracy : When no prior crop labels are available, the model groups farms by similarity in vegetation temporal profiles, yielding accuracies typically between 70% and 80%. While this approach provides valuable insights, it may occasionally merge phenologically similar crops or over-segment single crop types.
• Aditional Considerations :
  • Classification performance can be impacted by external factors such as cloud cover and the availability of clear temporal data within a single crop season.
  • The model focuses on crops grown within a single growing season and may not effectively classify crops with longer or overlapping phenological cycles.
  • The supervised model depends on the availability and quality of user-labeled reference farms for best results.

In summary, the Crop Classification model provides reliable and actionable crop type information at regional scales, supporting decision-making in agriculture, food supply chain management, and risk assessment with acceptable and interpretable accuracy metrics