ArticlePlant methods2021
Determination of wheat spike and spikelet architecture and grain traits using X-ray Computed Tomography imaging.
Article in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Quantifying wheat spike morphology by high resolution 3D surface scanning.Plant methods · 2026Article
- DiffPlantCT: a training-free, annotation-free approach to cross-species plant CT image segmentation.Plant methods · 2026Article
- Lipid MRI in plant science: principles and potential areas of application.Journal of experimental botany · 2026Review
- Technological advances in imaging and modelling of leaf structural traits: a review of heat stress in wheat.Journal of experimental botany · 2026Review
- Study on automatic detection of wheat spike grain number based on deep learning.Frontiers in plant science · 2026Article
- A Forward Genetics Strategy for High-Throughput Gene Identification via Precise Image-Based Phenotyping of an Indexed EMS Mutant Library.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Strigolactones coordinate barley tillering and grain size.Journal of experimental botany · 2025Article
- Morphological Acclimation of Durum Wheat Spikes in Response to Foliar Micronutrient Applications.Plants (Basel, Switzerland) · 2025Article
- Analysis of Wheat Spike Morphological Traits by 2D Imaging.Plant phenomics (Washington, D.C.) · 2025Article
- Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits.Plant methods · 2025Article
- MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.Journal of experimental botany · 2025Article
- Improved YOLO-FastestV2 wheat spike detection model based on a multi-stage attention mechanism with a LightFPN detection head.Frontiers in plant science · 2024Article
- Image facilitated assessment of intra-spike variation in grain size in wheat under high temperature and drought stress.Scientific reports · 2023Article
- Detection and characterization of spike architecture based on deep learning and X-ray computed tomography in barley.Plant methods · 2023Article
- Reviving grain quality in wheat through non-destructive phenotyping techniques like hyperspectral imaging.Food and energy security · 2023Review
- Unexpected Effects of Sulfate and Sodium Chloride Application on Yield Qualitative Characteristics and Symmetry Indicators of Hard and Soft Wheat Kernels.Plants (Basel, Switzerland) · 2023Article
- Application of potassium nitrate and salicylic acid improves grain yield and related traits by delaying leaf senescence inFrontiers in plant science · 2023Article
- X-ray driven peanut trait estimation: computer vision aided agri-system transformation.Plant methods · 2022Article
- Wheat grain width: a clue for re-exploring visual indicators of grain weight.Plant methods · 2022Article
- Detection method of wheat spike improved YOLOv5s based on the attention mechanism.Frontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundWheat spike architecture is a key determinant of multiple grain yield components and detailed examination of spike morphometric traits is beneficial to explain wheat grain yield and the effects of differing agronomy and genetics. However, quantification of spike morphometric traits has been very limited because it relies on time-consuming manual measurements.
resultsIn this study, using X-ray Computed Tomography imaging, we proposed a method to efficiently detect the 3D architecture of wheat spikes and component spikelets by clustering grains based on their Euclidean distance and relative positions. Morphometric characteristics of wheat spikelets and grains, e.g., number, size and spatial distribution along the spike can be determined. Two commercial wheat cultivars, one old, Maris Widgeon, and one modern, Siskin, were studied as examples. The average grain volume of Maris Widgeon and Siskin did not differ, but Siskin had more grains per spike and therefore greater total grain volume per spike. The spike length and spikelet number were not statistically different between the two cultivars. However, Siskin had a higher spikelet density (number of spikelets per unit spike length), with more grains and greater grain volume per spikelet than Maris Widgeon. Spatial distribution analysis revealed the number of grains, the average grain volume and the total grain volume of individual spikelets varied along the spike. Siskin had more grains and greater grain volumes per spikelet from spikelet 6, but not spikelet 1-5, compared with Maris Widgeon. The distribution of average grain volume along the spike was similar for the two wheat cultivars.
conclusionThe proposed method can efficiently extract spike, spikelet and grain morphometric traits of different wheat cultivars, which can contribute to a more detailed understanding of the sink of wheat grain yield.
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