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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
Yosuke Toda, Nagoya University, Japan
Submission Instructions
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 ([email protected]).
Submission Deadline: December 31, 2023
Table of Contents
- Tomato leaf diseases have a significant impact on tomato cultivation modernization. Object detection is an important technique for disease prevention since it may collect reliable disease information. Tomato leaf diseases occur in a variety of ...
- Automatically segmenting crops and weeds in the image input from cameras accurately is essential in various agricultural technology fields, such as herbicide spraying by farming robots based on crop and weed segmentation information. However, crop and ...
- The rise of self-supervised learning (SSL) methods in recent years presents an opportunity to leverage unlabeled and domain-specific datasets generated by image-based plant phenotyping platforms to accelerate plant breeding programs. Despite the surge of ...
- The tea yield estimation provides information support for the harvest time and amount and serves as a decision-making basis for farmer management and picking. However, the manual counting of tea buds is troublesome and inefficient. To improve the ...
- Developing automated soybean seed counting tools will help automate yield prediction before harvesting and improving selection efficiency in breeding programs. An integrated approach for counting and localization is ideal for subsequent analysis. The ...
- Silique morphology is an important trait that determines the yield output of oilseed rape (Brassica napus L.). Segmenting siliques and quantifying traits are challenging because of the complicated structure of an oilseed rape plant at the reproductive ...
- Head (panicle) density is a major component in understanding crop yield, especially in crops that produce variable numbers of tillers such as sorghum and wheat. Use of panicle density both in plant breeding and in the agronomy scouting of commercial crops ...
- In modern smart orchards, fruit detection models based on deep learning require expensive dataset labeling work to support the construction of detection models, resulting in high model application costs. Our previous work combined generative adversarial ...
