ArticlePlant methods2020
Wheat ear counting using K-means clustering segmentation and convolutional neural network.
Article in Plant methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- An intelligent method and platform for obtaining lettuce canopy coverage.Frontiers in plant science · 2026Article
- LGWheatNet: A Lightweight Wheat Spike Detection Model Based on Multi-Scale Information Fusion.Plants (Basel, Switzerland) · 2025Article
- CADFFNet: a dual-branch neural network for non-destructive detection of cigar leaf moisture content during air-curing stage.Frontiers in plant science · 2025Article
- A lightweight wheat ear counting model in UAV images based on improved YOLOv8.Frontiers in plant science · 2025Article
- A generalized model for accurate wheat spike detection and counting in complex scenarios.Scientific reports · 2024Article
- A density map-based method for counting wheat ears.Frontiers in plant science · 2024Article
- Improved YOLO-FastestV2 wheat spike detection model based on a multi-stage attention mechanism with a LightFPN detection head.Frontiers in plant science · 2024Article
- Segmentation and counting of wheat spike grains based on deep learning and textural feature.Plant methods · 2023Article
- Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet.Plant phenomics (Washington, D.C.) · 2023Article
- SPSI: A Novel Composite Index for Estimating Panicle Number in Winter Wheat before Heading from UAV Multispectral Imagery.Plant phenomics (Washington, D.C.) · 2023Article
- Genetic Inheritance of Stripe Rust (Plants (Basel, Switzerland) · 2022Article
- Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model.Frontiers in plant science · 2022Article
- Automatic Detection and Counting of Wheat Spikelet Using Semi-Automatic Labeling and Deep Learning.Frontiers in plant science · 2022Article
- Lightweight and efficient neural network with SPSA attention for wheat ear detection.PeerJ. Computer science · 2022Article
- Rapid Detection of Wheat Ears in Orthophotos From Unmanned Aerial Vehicles in Fields Based on YOLOX.Frontiers in plant science · 2022Article
- Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification.Plant phenomics (Washington, D.C.) · 2022Article
- Estimating and evaluating the rice cluster distribution uniformity with UAV-based images.Scientific reports · 2021Article
- Wheat Ear Recognition Based on RetinaNet and Transfer Learning.Sensors (Basel, Switzerland) · 2021Article
- Leaf area index estimation model for UAV image hyperspectral data based on wavelength variable selection and machine learning methods.Plant methods · 2021Article
- Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning.Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWheat yield is influenced by the number of ears per unit area, and manual counting has traditionally been used to estimate wheat yield. To realize rapid and accurate wheat ear counting, K-means clustering was used for the automatic segmentation of wheat ear images captured by hand-held devices. The segmented data set was constructed by creating four categories of image labels: non-wheat ear, one wheat ear, two wheat ears, and three wheat ears, which was then was sent into the convolution neural network (CNN) model for training and testing to reduce the complexity of the model.
resultsThe recognition accuracy of non-wheat, one wheat, two wheat ears, and three wheat ears were 99.8, 97.5, 98.07, and 98.5%, respectively. The model
conclusionsThe classification of segmented images as opposed to target recognition not only reduces the workload of manual annotation but also improves significantly the efficiency and accuracy of wheat ear counting, thus meeting the requirements of wheat yield estimation in the field environment.
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