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Cropformer: A new generalized deep learning classification approach for multi-scenario crop classification.

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  • 1College of Land Science and Technology, China Agricultural University, Beijing, China.

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|March 20, 2023
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Summary

Cropformer, a novel deep learning model, enhances crop classification accuracy across multiple scenarios by integrating global and local features. This approach improves yield estimation and classification performance, even with limited data.

Keywords:
Cropformerdeep learningmulti-scenario crop classificationpre-trainingtime series

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Accurate crop classification is vital for yield estimation.
  • Existing methods struggle with multi-scenario crop classification.
  • Current approaches often extract only single types of features.

Purpose of the Study:

  • To develop a deep learning approach for multi-scenario crop classification.
  • To address limitations of single-feature extraction in crop classification.
  • To improve the accuracy and efficiency of crop classification using remotely sensed data.

Main Methods:

  • Proposed Cropformer, a two-step deep learning model.
  • Utilized self-supervised pre-training on unlabeled time series data for knowledge accumulation.
  • Employed fine-tuned supervised classification on labeled time series data.
  • Extracted both global and local features for comprehensive analysis.

Main Results:

  • Cropformer achieved significant accuracy advantages in multi-scenario crop classification.
  • The model demonstrated higher accuracy with fewer samples (few-sample classification).
  • Cropformer exhibited outstanding performance in model transfer and classification efficiency.
  • Experimental validation across five diverse study areas confirmed effectiveness.

Conclusions:

  • Cropformer effectively builds prior knowledge using unlabeled data.
  • The model learns generalized features, enhancing applicability across multiple scenarios.
  • This approach offers a robust solution for accurate and efficient crop classification.