ArticlePlant phenomics (Washington, D.C.)2023
Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.Plant communications · 2026Review
- Predictions of wheat phenotypic variability by integrating high-throughput phenotyping observations into a crop growth model.Plant phenomics (Washington, D.C.) · 2026Article
- An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences.NAR genomics and bioinformatics · 2026Article
- Deep learning in plant phenotyping: the first ten years.Plant phenomics (Washington, D.C.) · 2025Review
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- LeafGen: Structure-aware Leaf Image Generation for Annotation-free Leaf Instance Segmentation.Plant phenomics (Washington, D.C.) · 2025Article
- Global rice multiclass segmentation dataset (RiceSEG): comprehensive and diverse high-resolution RGB-annotated images for the development and benchmarking of rice segmentation algorithms.Plant phenomics (Washington, D.C.) · 2025Article
- Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review.Plant phenomics (Washington, D.C.) · 2025Review
- An improved YOLOv5s-based method for detecting rice leaves in the field.Frontiers in plant science · 2025Article
- A dual-task segmentation network based on multi-head hierarchical attention for 3D plant point cloud.Frontiers in plant science · 2025Article
- A spontaneous keypoints connection algorithm for leafy plants skeletonization and phenotypes extraction.Frontiers in plant science · 2025Article
- Crops3D: a diverse 3D crop dataset for realistic perception and segmentation toward agricultural applications.Scientific data · 2024Article
- Lightweight Corn Leaf Detection and Counting Using Improved YOLOv8.Sensors (Basel, Switzerland) · 2024Article
- Efficient Wheat Head Segmentation with Minimal Annotation: A Generative Approach.Journal of imaging · 2024Article
- Time-Series Field Phenotyping of Soybean Growth Analysis by Combining Multimodal Deep Learning and Dynamic Modeling.Plant phenomics (Washington, D.C.) · 2024Article
- In Situ Root Dataset Expansion Strategy Based on an Improved CycleGAN Generator.Plant phenomics (Washington, D.C.) · 2024Article
- Semi-supervised Counting of Grape Berries in the Field Based on Density Mutual Exclusion.Plant phenomics (Washington, D.C.) · 2023Article
- Enhancing Green Fraction Estimation in Rice and Wheat Crops: A Self-Supervised Deep Learning Semantic Segmentation Approach.Plant phenomics (Washington, D.C.) · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The number of leaves at a given time is important to characterize plant growth and development. In this work, we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images. The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages (150,000 images with over 2 million labels). The realism of the images was then improved using domain adaptation methods before training deep learning models. The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset, collecting measurements from 5 countries obtained under different environments, growth stages, and lighting conditions with different cameras (450 images with over 2,162 labels). Among the 6 combinations of deep learning models and domain adaptation techniques, the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.