Advances in Proximal Sensing and Image Analysis for Time-Series Phenotyping

Image of a field with sensing equipment

Plant Phenomics, a Science Partner Journal, is now accepting submissions for a special issue titled Advances in Proximal Sensing and Image Analysis for Time-Series Phenotyping.

Scope

Plant phenotyping is widely known as the basis of plant phenomics, which is the bridge as well as a bottleneck for studying the interactions between genomics and the environment. Recently, time-series phenotyping is gaining more and more attention due to its potential in revealing key trait differences in critical growth periods as well as a way to access more functional traits. Meanwhile, proximal sensing and image analysis have brought a renaissance in plant phenotyping. It is possible to combine multi-source proximal sensing techniques, such as RGB, light detection and ranging (LiDAR), multi/hyperspectral, solar-induced fluorescence (SIF), and thermal sensors, to better describe plant growth and development. Image analysis, especially computer vision and data-driven deep learning methods, can make phenotyping more objective, accurate, and efficient than traditional field measurement. Therefore, combining advanced proximal sensing and image analysis will provide a comprehensive study of time-series plant phenotyping.

Given the above context, this special issue invites submissions broadly contributing to time-series plant phenotyping. Specific topics of interest include:

  1. Data fusion of multi-source proximal sensing imagery to provide consistent spatial and temporal support.
  2. Methods in time-series phenotyping including object detection, tracking, trait extraction, etc.
  3. Application of time-series imagery to plant breeding, cultivation, and management.

Articles must be original research, not published elsewhere. All articles will go through a rigorous peer-review process as per the journal norms. Review articles around the topics are also encouraged. Articles must follow the instructions for authors at https://spj.sciencemag.org/journals/plantphenomics/guidelines/.

    Guest Editors

    Shichao Jin, Nanjing Agricultural University

    Yanjun Su, Institute of Botany, Chinese Academy of Sciences

    Changying Li, University of Georgia

    Qinghua Guo, Peking University

      Submission Deadline

      June 30, 2023. All papers will be published online after acceptance.

        Submission Instructions

        Please submit the full manuscript to Plant Phenomics via our online submission system. When submitting, please indicate in your cover letter that your submission is intended for consideration for the special issue, Advances in Proximal Sensing and Image Analysis for Time-Series Phenotyping. For inquiries, please contact Dr. Shichao Jin (jschaon@njau.edu.cn).

        Table of Contents

        As articles within the special issue are published they will appear below.


        Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits

        Meiyan Shu, Shuaipeng Fei, Bingyu Zhang, Xiaohong Yang, Yan Guo, Baoguo Li, Yuntao Ma

        Plant Phenomics, vol. 2022, Article ID 9802585, 17 pages | Aug 28, 2022


        3dCAP-Wheat: An Open-Source Comprehensive Computational Framework Precisely Quantifies Wheat Foliar, Nonfoliar, and Canopy Photosynthesis

        Tian-Gen Chang, Zai Shi, Honglong Zhao, Qingfeng Song, Zhonghu He, Jeroen Van Rie, Bart Den Boer, Alexander Galle, and Xin-Guang Zhu

        Plant Phenomics, Vol. 2022, Article ID 9758148 | Jul 21, 2022


        Shortwave Radiation Calculation for Forest Plots Using Airborne LiDAR Data and Computer Graphics

        Xinbo Xue, Shichao Jin, Feng An, Huaiqing Zhang, Jiangchuan Fan, Markus P. Eichhorn, Chengye Jin, Bangqian Chen, Ling Jiang, and Ting Yun

        Plant Phenomics, Vol. 2022, Article ID 9856739 | Jul 18, 2022


        PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants

        Dawei Li, Jinsheng Li, Shiyu Xiang, and Anqi Pan

        Plant Phenomics, Vol. 2022, Article ID 9787643, 20 pages | May 23, 2022


        Simultaneous Prediction of Wheat Yield and Grain Protein Content Using Multitask Deep Learning from Time-Series Proximal Sensing

        Zhuangzhuang Sun, Qing Li, Shichao Jin, Yunlin Song, Shan Xu, Xiao Wang, Jian Cai, Qin Zhou, Yan Ge, Ruinan Zhang, Jingrong Zang, and Dong Jiang

        Plant Phenomics, Vol. 2022, Article ID 9757948, 13 pages | Mar 29, 2022