Object Detection and Image Segmentation for Plant Phenotyping

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Plant Phenomics, a Science Partner Journal, is now accepting submissions for a special issue titled Object Detection and Image Segmentation for Plant Phenotyping.

Scope

Object detection and image segmentation are important research topics in computer vision and have been widely applied in real-world applications. Recently, object detection and image segmentation using deep learning techniques have played important roles in plant phenotyping tasks, such as identifying crop diseases and insects and measuring and counting plant organs (leaves, stems, fruits, etc.). However, these techniques also have certain unavoidable drawbacks; for example, acquiring, annotating, and maintaining large datasets for phenotyping tasks are still difficult and expensive. They are also often subjected to specific environmental conditions and target species.

This special issue welcomes original research articles, review articles, perspectives, and database/software articles related to object detection and image segmentation and related methodologies, tools, and datasets.

Specific topics of interest include 2- and 3-dimensional-based:

  • Object detection, segmentation, tracking
  • Domain adaptation
  • Synthetic data generation
  • Unsupervised/self-supervised learning
  • Multiple scales (spatial, reflectance) data fusion

    Guest Editors

    Wei Guo, University of Tokyo, Japan

    Ian Stavness, University of Saskatchewan, Canada

    Etienne David, Hiphen, France

    Wenli Zhang, Beijing University of Technology, China

      Submission Deadline

      December 31, 2023. All papers will be published online after acceptance.

        Submission Instructions

        Submissions must follow the instructions for authors. 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, “Object Detection and Image Segmentation for Plant Phenotyping”. For inquiries, please contact Dr. Wei Guo (guowei@g.ecc.u-tokyo.ac.jp).

        Table of Contents

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


        EasyDAM_V2: Efficient Data Labeling Method for Multishape, Cross-Species Fruit Detection

        Wenli Zhang, Kaizhen Chen, Chao Zheng, Yuxin Liu, Wei Guo

        Plant Phenomics, vol. 2022, Article ID 9761674, 16 pages, 2022