Agriculture

PhenoSR: Enhancing organ-level phenotyping with super-resolution RGB UAV imagery for large-scale field experiments

Abstract

This paper presents PhenoSR, a method for enhancing organ-level phenotyping with super-resolution RGB UAV imagery for large-scale field experiments.

Key Contributions

  • Developed super-resolution techniques for RGB UAV imagery
  • Enhanced organ-level phenotyping capabilities
  • Demonstrated effectiveness for large-scale field experiments

Citation 

@article{zhang2025phenosr,
  title={PhenoSR: Enhancing organ-level phenotyping with super-resolution RGB UAV imagery for large-scale field experiments},
  author={Zhang, Ruinan and Jin, Shuai and Wang, Yue and others},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={228},
  pages={582--602},
  year={2025},
  publisher={Elsevier}
}

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}
}

Enhancing wheat crop physiology monitoring through spectroscopic analysis of stomatal conductance dynamics

Abstract

This paper presents a spectroscopic analysis approach for enhancing wheat crop physiology monitoring through stomatal conductance dynamics.

Citation 

@article{cheng2024enhancing,
  title={Enhancing wheat crop physiology monitoring through spectroscopic analysis of stomatal conductance dynamics},
  author={Cheng, KH and Sun, Zhuangzhuang and Zhong, Wanlu and Wang, Zhihui and Visser, Marco and Liu, Shuwen and Yan, Zhengbing and Zhao, Yingyi and Zhang, Ruinan and Zang, Jingrong and others},
  journal={Remote Sensing of Environment},
  volume={312},
  pages={114325},
  year={2024},
  publisher={Elsevier}
}

Comparison of Different Machine Learning Algorithms for the Prediction of the Wheat Grain Filling Stage Using RGB Images

Abstract

This paper presents a comparison of different machine learning algorithms for the prediction of the wheat grain filling stage using RGB images.

Citation 

@article{song2023comparison,
  title={Comparison of Different Machine Learning Algorithms for the Prediction of the Wheat Grain Filling Stage Using RGB Images},
  author={Song, Yunlin and Sun, Zhuangzhuang and Zhang, Ruinan and Min, Haijiang and Li, Qing and Cai, Jian and Wang, Xiao and Zhou, Qin and Jiang, Dong},
  journal={Plants},
  volume={12},
  number={23},
  pages={4043},
  year={2023},
  publisher={MDPI}
}

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}
}

Anti-gravity stem-seeking restoration algorithm for maize seed root image phenotype detection

Abstract

This paper presents an anti-gravity stem-seeking restoration algorithm for maize seed root image phenotype detection.

Citation 

@article{mingxuan2022anti,
  title={Anti-gravity stem-seeking restoration algorithm for maize seed root image phenotype detection},
  author={Mingxuan, Zou and Wei, Lu and Hui, Luo and Ruinan, Zhang and Yiming, Deng},
  journal={Computers and Electronics in Agriculture},
  volume={202},
  pages={107337},
  year={2022},
  publisher={Elsevier}
}