ArticleSensors (Basel, Switzerland)2021
Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Early Plant Development as a Systems-Level Trait: Integrating Omics, Artificial Intelligence, and Emerging Biotechnologies.Plants (Basel, Switzerland) · 2026Review
- Smart Image-Based Deep Learning System for Automated Quality Grading of Phalaenopsis Seedlings in Outsourced Production.Sensors (Basel, Switzerland) · 2025Article
- OneRosette to predict them all: single plant prompting on a visual foundation model to segment symptomatic Arabidopsis thaliana time series.Plant methods · 2025Article
- Bayesian adaptive sampling: A smart approach for affordable germination phenotyping.Plant phenomics (Washington, D.C.) · 2025Article
- Deep-learning-ready RGB-depth images of seedling development.Plant methods · 2025Article
- Affordable Phenotyping at the Edge for High-Throughput Detection of Hypersensitive Reaction Involving Cotyledon Loss.Plant phenomics (Washington, D.C.) · 2024Article
- Classification of plug seedling quality by improved convolutional neural network with an attention mechanism.Frontiers in plant science · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day time. RGB-Depth imaging devices are also accessible at low-cost and this opens opportunities to extend the monitoring of seedling during days and nights. In this article, we investigate the added value to fuse RGB imaging with depth imaging for this task of seedling growth stage monitoring. We propose a deep learning architecture along with RGB-Depth fusion to categorize the three first stages of seedling growth. Results show an average performance improvement of 5% correct recognition rate by comparison with the sole use of RGB images during the day. The best performances are obtained with the early fusion of RGB and Depth. Also, Depth is shown to enable the detection of growth stage in the absence of the light.
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