ArticleFrontiers in plant science2022
High-Precision Wheat Head Detection Model Based on One-Stage Network and GAN Model.
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 17 papers.
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17 citing papers in PubMed.
- High-Precision Pest Management Based on Multimodal Fusion and Attention-Guided Lightweight Networks.Insects · 2025Article
- A CNN-Transformer Hybrid Framework for Multi-Label Predator-Prey Detection in Agricultural Fields.Sensors (Basel, Switzerland) · 2025Article
- RGB imaging and computer vision-based approaches for identifying spike number loci for wheat.Plant phenomics (Washington, D.C.) · 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
- Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems.Plants (Basel, Switzerland) · 2024Article
- WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network.Plant methods · 2023Article
- High-Accuracy Maize Disease Detection Based on Attention Generative Adversarial Network and Few-Shot Learning.Plants (Basel, Switzerland) · 2023Article
- A Novel Deep Learning Model for Accurate Pest Detection and Edge Computing Deployment.Insects · 2023Article
- A Computer Vision-Based Automatic System for Egg Grading and Defect Detection.Animals : an open access journal from MDPI · 2023Article
- High-Precision Tomato Disease Detection Using NanoSegmenter Based on Transformer and Lightweighting.Plants (Basel, Switzerland) · 2023Article
- A High-Precision Plant Disease Detection Method Based on a Dynamic Pruning Gate Friendly to Low-Computing Platforms.Plants (Basel, Switzerland) · 2023Article
- A High Performance Wheat Disease Detection Based on Position Information.Plants (Basel, Switzerland) · 2023Article
- High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm.Plants (Basel, Switzerland) · 2023Article
- Point clouds segmentation of rapeseed siliques based on sparse-dense point clouds mapping.Frontiers in plant science · 2023Article
- A High-Precision Method for 100-Day-Old Classification of Chickens in Edge Computing Scenarios Based on Federated Computing.Animals : an open access journal from MDPI · 2022Article
- A High-Performance Day-Age Classification and Detection Model for Chick Based on Attention Encoder and Convolutional Neural Network.Animals : an open access journal from MDPI · 2022Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Counting wheat heads is a time-consuming process in agricultural production, which is currently primarily carried out by humans. Manually identifying wheat heads and statistically analyzing the findings has a rigorous requirement for the workforce and is prone to error. With the advancement of machine vision technology, computer vision detection algorithms have made wheat head detection and counting feasible. To accomplish this traditional labor-intensive task and tackle various tricky matters in wheat images, a high-precision wheat head detection model with strong generalizability was presented based on a one-stage network structure. The model's structure was referred to as that of the YOLO network; meanwhile, several modules were added and adjusted in the backbone network. The one-stage backbone network received an attention module and a feature fusion module, and the Loss function was improved. When compared to various other mainstream object detection networks, our model outperforms them, with a
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