Abstract

This paper presents PhenoNet, a two-stage lightweight deep learning framework for real-time wheat phenophase classification using remote sensing imagery.

Key Contributions

• Developed a lightweight deep learning architecture for real-time processing
• Achieved high accuracy in wheat phenophase classification
• Demonstrated practical applicability for agricultural monitoring

Citation

@article{zhang2024phenonet,
  title={PhenoNet: A two-stage lightweight deep learning framework for real-time wheat phenophase classification},
  author={Zhang, Ruinan and Jin, Shuai and Zhang, Yue and others},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={208},
  pages={136--157},
  year={2024},
  publisher={Elsevier}
}