ArticleFrontiers in plant science2022
Automatic Detection and Counting of Wheat Spikelet Using Semi-Automatic Labeling and Deep Learning.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Quantifying wheat spike morphology by high resolution 3D surface scanning.Plant methods · 2026Article
- Wheat spike and spikelet detection and counting from high-resolution digital imagery using YOLO with Oriented Bounding Boxes.Scientific reports · 2026Article
- High-throughput phenotyping of wheat ear surface area and ear density in the field.Plant phenomics (Washington, D.C.) · 2026Article
- An Effective YOLOv11 Grain Detection Model Trained on Intact Barley Spikes Reveals a QTL Containing a Pivotal Regulator of Lateral Spikelet Formation.Plants (Basel, Switzerland) · 2026Article
- Analysis of Wheat Spike Morphological Traits by 2D Imaging.Plant phenomics (Washington, D.C.) · 2025Article
- A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs.Plants (Basel, Switzerland) · 2025Article
- Evaluation of the Spike Diversity of Seven Hexaploid Wheat Species and an Artificial Amphidiploid Using a Quadrangle Model Obtained from 2D Images.Plants (Basel, Switzerland) · 2024Article
- A co-located QTL for seven spike architecture-related traits shows promising breeding use potential in common wheat (Triticum aestivum L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- YOLO SSPD: a small target cotton boll detection model during the boll-spitting period based on space-to-depth convolution.Frontiers in plant science · 2024Article
- PanicleNeRF: Low-Cost, High-Precision In-Field Phenotyping of Rice Panicles with Smartphone.Plant phenomics (Washington, D.C.) · 2024Article
- Segmentation and counting of wheat spike grains based on deep learning and textural feature.Plant methods · 2023Article
- Image-based classification of wheat spikes by glume pubescence using convolutional neural networks.Frontiers in plant science · 2023Article
Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The number of wheat spikelets is an important phenotypic trait and can be used to assess the grain yield of the wheat crop. However, manual counting of spikelets is time-consuming and labor-intensive. To develop a cost-effective and highly efficient phenotyping system for counting the number of spikelets under laboratory conditions, methods based on imaging processing techniques and deep learning were proposed to accurately detect and count spikelets from color images of wheat spikes captured at the grain filling stage. An unsupervised learning-based method was first developed to automatically detect and label spikelets from spike color images and build the datasets for the model training. Based on the constructed datasets, a deep convolutional neural network model was retrained using transfer learning to detect the spikelets. Testing results showed that the root mean squared errors, relative root mean squared errors, and the coefficients of determination between the automatic and manual counted spikelets for four wheat lines were 0.62, 0.58, 0.54, and 0.77; 3.96, 3.73, 3.34, and 4.94%; and 0.73, 0.78, 0.84, and 0.67, respectively. We demonstrated that the proposed methods can effectively estimate the number of wheat spikelets, which improves the counting efficiency of wheat spikelets and contributes to the analysis of the developmental characteristics of wheat spikes.
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