ArticleFrontiers in plant science2022
Detection method of wheat spike improved YOLOv5s based on the attention mechanism.
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 20 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- LiteMS-YOLO: a lightweight framework for small target detection in complex wheat field environments.Frontiers in plant science · 2026Article
- Study on automatic detection of wheat spike grain number based on deep learning.Frontiers in plant science · 2026Article
- RGB imaging and computer vision-based approaches for identifying spike number loci for wheat.Plant phenomics (Washington, D.C.) · 2025Article
- GrainNet: efficient detection and counting of wheat grains based on an improved YOLOv7 modeling.Plant methods · 2025Article
- Article
- CropPhenoX: high-throughput automatic extraction system for wheat seedling phenotypic traits based on software and hardware collaboration.Frontiers in plant science · 2025Article
- Research on detection and counting method of wheat ears in the field based on YOLOv11-EDS.Frontiers in plant science · 2025Article
- Automatic detection and counting of wheat seedling based on unmanned aerial vehicle images.Frontiers in plant science · 2025Article
- Enhancing Wheat Spike Counting and Disease Detection Using a Probability Density Attention Mechanism in Deep Learning Models for Precision Agriculture.Plants (Basel, Switzerland) · 2024Article
- Automatic detection and counting of wheat spike based on DMseg-Count.Scientific reports · 2024Article
- A generalized model for accurate wheat spike detection and counting in complex scenarios.Scientific reports · 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
- Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming.Frontiers in plant science · 2024Article
- A maize seed variety identification method based on improving deep residual convolutional network.Frontiers in plant science · 2024Article
- CSNet: A Count-Supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting.Plant phenomics (Washington, D.C.) · 2024Article
- WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network.Plant methods · 2023Article
- Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients' Nutrition Control.Sensors (Basel, Switzerland) · 2023Article
- TasselLFANet: a novel lightweight multi-branch feature aggregation neural network for high-throughput image-based maize tassels detection and counting.Frontiers in plant science · 2023Article
- Application of improved YOLOv7-based sugarcane stem node recognition algorithm in complex environments.Frontiers in plant science · 2023Article
- Unstructured road extraction and roadside fruit recognition in grape orchards based on a synchronous detection algorithm.Frontiers in plant science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In wheat breeding, spike number is a key indicator for evaluating wheat yield, and the timely and accurate acquisition of wheat spike number is of great practical significance for yield prediction. In actual production; the method of using an artificial field survey to count wheat spikes is time-consuming and labor-intensive. Therefore, this paper proposes a method based on YOLOv5s with an improved attention mechanism, which can accurately detect the number of small-scale wheat spikes and better solve the problems of occlusion and cross-overlapping of the wheat spikes. This method introduces an efficient channel attention module (ECA) in the C3 module of the backbone structure of the YOLOv5s network model; at the same time, the global attention mechanism module (GAM) is inserted between the neck structure and the head structure; the attention mechanism can be more Effectively extract feature information and suppress useless information. The result shows that the accuracy of the improved YOLOv5s model reached 71.61% in the task of wheat spike number, which was 4.95% higher than that of the standard YOLOv5s model and had higher counting accuracy. The improved YOLOv5s and YOLOv5m have similar parameters, while RMSE and MEA are reduced by 7.62 and 6.47, respectively, and the performance is better than YOLOv5l. Therefore, the improved YOLOv5s method improves its applicability in complex field environments and provides a technical reference for the automatic identification of wheat spike numbers and yield estimation. Labeled images, source code, and trained models are available at: https://github.com/228384274/improved-yolov5.
Indexed as
Identifiers
What OpenQuestion holds
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.