ArticleFrontiers in plant science2020
High-Throughput
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
36 citing papers in PubMed.
- Advances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.Journal of experimental botany · 2026Review
- Engineering climate-resilient horticultural crops: advances in transcriptional regulation, genome editing, and synthetic networks.Horticulture research · 2026Article
- Image-based high-throughput phenotyping enables genetic analyses of pod morphological traits in mungbean (Vigna radiata (L.) R. Wilczek).G3 (Bethesda, Md.) · 2026Article
- A systematic comparison of transformers and ConvNets for root segmentation across nine datasets.Plant methods · 2026Article
- Dual-guided asymmetric MP-former for rice root instance segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- Root segmentation beyond species boundaries: A generalizable framework for anatomical analysis.Plant phenomics (Washington, D.C.) · 2026Article
- Automatic root measurement: a lightweight method for measuring pea root length.Plant methods · 2025Article
- The Application of Supervised Machine Learning Algorithms for Image Alignment in Multi-Channel Imaging Systems.Sensors (Basel, Switzerland) · 2025Article
- Deep Learning-Based Automated Approach for Determination of Pig Carcass Traits.Animals : an open access journal from MDPI · 2024Article
- Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X-ray computed tomography volumes with high noise levels.Plant methods · 2024Article
- In Situ Root Dataset Expansion Strategy Based on an Improved CycleGAN Generator.Plant phenomics (Washington, D.C.) · 2024Article
- Improved Transformer for Time Series Senescence Root Recognition.Plant phenomics (Washington, D.C.) · 2024Article
- Rice breeding for low input agriculture.Frontiers in plant science · 2024Review
- The State of the Art in Root System Architecture Image Analysis Using Artificial Intelligence: A Review.Plant phenomics (Washington, D.C.) · 2024Review
- SDC-DeepLabv3+: Lightweight and Precise Localization Algorithm for Safflower-Harvesting Robots.Plant phenomics (Washington, D.C.) · 2024Article
- Semantic segmentation of plant roots from RGB (mini-) rhizotron images-generalisation potential and false positives of established methods and advanced deep-learning models.Plant methods · 2023Article
- Automatic Segmentation of Hyperreflective Foci in OCT Images Based on Lightweight DBR Network.Journal of digital imaging · 2023Article
- Application of a linear interpolation algorithm in radiation therapy dosimetry for 3D dose point acquisition.Scientific reports · 2023Article
- Explainable deep learning in plant phenotyping.Frontiers in artificial intelligence · 2023Review
- A method of cotton root segmentation based on edge devices.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Rhizotrons method is an important means of detecting dynamic growth and development phenotypes of plant roots. However, the segmentation of root images is a critical obstacle restricting further development of this method. At present, researchers mostly use direct manual drawings or software-assisted manual drawings to segment root systems for analysis. Root systems can be segmented from root images obtained by the Rhizotrons method, and then, root system lengths and diameters can be obtained with software. This type of image segmentation method is extremely inefficient and very prone to human error. Here, we investigate the effectiveness of an automated image segmentation method based on the DeepLabv3+ convolutional neural network (CNN) architecture to streamline such measurements. We have improved the upsampling portion of the DeepLabv3+ network and validated it using
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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.