ArticleGigaScience2021
ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture.
Article in GigaScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 58 citations in OpenAlex.
- Advances in genomics-driven genetic decoding and genomic design breeding in tomato.Horticulture research · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Article
- The Green Index: A Widely Accessible Method to Quantify the Degree of Greenness of Photosynthetic Organisms.Plant, cell & environment · 2025Article
- Improved U-Shaped Convolutional Neural Network with Convolutional Block Attention Module and Feature Fusion for Automated Segmentation of Fine Roots in Field Rhizotron Imagery.Sensors (Basel, Switzerland) · 2025Article
- The transcription factor NF-YA10 determines the area explored by Arabidopsis thaliana roots and directly regulates LAZY genes.The Plant journal : for cell and molecular biology · 2025Article
- Research on Fine-Grained Phenotypic Analysis of Temporal Root Systems - Improved YoloV8seg Applied for Fine-Grained Analysis of In Situ Root Temporal Phenotypes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Variation in forest root image annotation by experts, novices, and AI.Plant methods · 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
- Explainable deep learning in plant phenotyping.Frontiers in artificial intelligence · 2023Review
- Application of Improved UNet and EnglightenGAN for Segmentation and Reconstruction of In Situ Roots.Plant phenomics (Washington, D.C.) · 2023Article
- Plant Root Phenotyping Using Deep Conditional GANs and Binary Semantic Segmentation.Sensors (Basel, Switzerland) · 2022Article
- High-throughput and automatic structural and developmental root phenotyping on Arabidopsis seedlings.Plant methods · 2022Article
- RootPainter: deep learning segmentation of biological images with corrective annotation.The New phytologist · 2022Article
- Machine Learning and 3D Reconstruction of Materials Surface for Nondestructive Inspection.Sensors (Basel, Switzerland) · 2022Article
- Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits.Plant methods · 2022Article
- Recent advances in methods forPeerJ · 2022Review
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system that combines 3D-printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium.
resultsWe developed a novel deep learning-based root extraction method that leverages the latest advances in convolutional neural networks for image segmentation and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals.
conclusionsOur work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies, as well as the screening of clock-related mutants, revealing novel root traits.
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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.