Deep Learning

SenNet: A dual-branch image semantic segmentation network for wheat senescence evaluation and high-yielding variety screening

Abstract

This paper presents SenNet, a dual-branch image semantic segmentation network for wheat senescence evaluation and high-yielding variety screening.

Citation 

@article{yao2025sennet,
  title={SenNet: A dual-branch image semantic segmentation network for wheat senescence evaluation and high-yielding variety screening},
  author={Yao, Jiaqi and Jin, Shichao and Zang, Jingrong and Zhang, Ruinan and Wang, Yu and Su, Yanjun and Guo, Qinghua and Ding, Yanfeng and Jiang, Dong},
  journal={Computers and Electronics in Agriculture},
  volume={237},
  pages={110632},
  year={2025},
  publisher={Elsevier}
}

OSNet: an oriented instance segmentation network of breeding plot extraction from UAV RGB imagery

Abstract

This paper presents OSNet, an oriented instance segmentation network for breeding plot extraction from UAV RGB imagery.

Key Contributions

  • Developed an oriented instance segmentation network for breeding plot extraction
  • Achieved high accuracy in plot boundary detection from UAV RGB imagery
  • Demonstrated effectiveness for agricultural field management

Citation 

@article{zhang2025osnet,
  title={OSNet: an oriented instance segmentation network of breeding plot extraction from UAV RGB imagery},
  author={Zhang, Ruinan and Zhang, Yue and Jin, Shuai and others},
  journal={Computers and Electronics in Agriculture},
  volume={236},
  pages={110436},
  year={2025},
  publisher={Elsevier}
}

PhenoNet: A two-stage lightweight deep learning framework for real-time wheat phenophase classification

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}
}

Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing

Abstract

This paper presents a multitask deep learning approach for simultaneous prediction of wheat yield and grain protein content using time-series proximal sensing data.

Citation 

@article{sun2022simultaneous,
  title={Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing},
  author={Sun, Zhuangzhuang and Li, Qing and Jin, Shichao and Song, Yunlin and Xu, Shan and Wang, Xiao and Cai, Jian and Zhou, Qin and Ge, Yan and Zhang, Ruinan and others},
  journal={Plant Phenomics},
  year={2022},
  publisher={AAAS}
}