Phenotyping

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}
}

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}
}

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}
}