ArticlePlant phenomics (Washington, D.C.)2020
A Deep Learning-Based Phenotypic Analysis of Rice Root Distribution from Field Images.
Article in Plant phenomics (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Advances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.Journal of experimental botany · 2026Review
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Ultra-Efficient Saline Soil Remediation with Passive Multistage Solar Distiller via Water Recycling.Nano-micro letters · 2026Article
- Non-Invasive Phenotyping of Sugar Beet and Maize Roots Using Field-Scale Spectral Electrical Impedance Tomography.Plant, cell & environment · 2025Article
- High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.Plants (Basel, Switzerland) · 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
- Non-destructive real-time monitoring of underground root development with distributed fiber optic sensing.Plant methods · 2024Article
- Phenotypic quantification of root spatial distribution along circumferential direction for field paddy-wheat.PloS one · 2023Article
- TrichomeYOLO: A Neural Network for Automatic Maize Trichome Counting.Plant phenomics (Washington, D.C.) · 2023Article
- Soybean Seedling Root Segmentation Using Improved U-Net Network.Sensors (Basel, Switzerland) · 2022Article
- Phenotyping for waterlogging tolerance in crops: current trends and future prospects.Journal of experimental botany · 2022Review
- Improving the efficiency of plant root system phenotyping through digitization and automation.Breeding science · 2022Article
- Objective Phenotyping of Root System Architecture Using Image Augmentation and Machine Learning in Alfalfa (Medicago sativa L.).Plant phenomics (Washington, D.C.) · 2022Article
- Root phenotyping: important and minimum information required for root modeling in crop plants.Breeding science · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Root distribution in the soil determines plants' nutrient and water uptake capacity. Therefore, root distribution is one of the most important factors in crop production. The trench profile method is used to observe the root distribution underground by making a rectangular hole close to the crop, providing informative images of the root distribution compared to other root phenotyping methods. However, much effort is required to segment the root area for quantification. In this study, we present a promising approach employing a convolutional neural network for root segmentation in trench profile images. We defined two parameters, Depth50 and Width50, representing the vertical and horizontal centroid of root distribution, respectively. Quantified parameters for root distribution in rice (
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