ReviewSensors (Basel, Switzerland)2014
A review of imaging techniques for plant phenotyping.
Review in Sensors (Basel, Switzerland), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 294 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
294 citing papers in PubMed.
- BN-NeRF: A fast 3D reconstruction and phenotyping framework for banana plants using handheld devices.Plant phenomics (Washington, D.C.) · 2026Article
- Structured illumination microscopy for high SNR 3D imaging from millimeter-thick tissues to cellular dynamics.Innovation (Cambridge (Mass.)) · 2026Article
- Turbocharging crop breeding with integrated biotechnology for a climate-resilient future.Journal of integrative plant biology · 2026Review
- Wheat Biofortification for Enhancing Iron, Zinc, and Protein: The Role of Mutation Breeding.Plants (Basel, Switzerland) · 2026Review
- High-Throughput Phenotyping: Status and Applications in Rice Breeding.Plants (Basel, Switzerland) · 2026Review
- Development of a New Handheld Device for Measuring Photosynthetic Carbon Dioxide Assimilation in Plant Leaves.Plants (Basel, Switzerland) · 2026Article
- Leaf temperature and its departure from ambient air temperature.Nature plants · 2026Review
- Advancements in 3D Reconstruction for Plant Phenotyping: Technologies, Applications, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- An AI-Driven Dual-Spectral Vision-Language Sensing Framework for Intelligent Agricultural Phenotyping.Sensors (Basel, Switzerland) · 2026Article
- Three-dimensional phenotyping: Technological advances and applications in genomics-assisted crop breeding.Plant communications · 2026Review
- Advanced Spectroscopic, Imaging, and Nanotechnology Tools for Diagnosing Fungal Diseases in Fruits.Food science & nutrition · 2026Review
- Development and Field Validation of a Smartphone-Based Web Application for Diagnosing Optimal Timing of Mid-Season Drainage in Rice Cultivation via Canopy Image-Derived Tiller Estimation.Sensors (Basel, Switzerland) · 2026Article
- Direct UAV-Based Detection ofSensors (Basel, Switzerland) · 2026Article
- Effect of Silicon on Early Root and Shoot Phenotypes of Rice in Hydroponic and Soil Systems.Plants (Basel, Switzerland) · 2026Article
- Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- A growth-year classification model for cultivated ginseng based on feature-decision-level fusion of leaf hyperspectral and RGB image features.Frontiers in plant science · 2026Article
- From 3DGS scenes to plant traits: a scalable extraction and segmentation framework for muskmelon phenotyping.Frontiers in plant science · 2026Article
- Chili-Seg: a high-precision detection and instance segmentation model for chili flowers in complex environments.Frontiers in plant science · 2026Article
- Morphometric trait analysis and machine learning-based yield modeling in wood apple (Feronia limonia L.).BMC plant biology · 2025Article
234 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.
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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.