ArticleScientific reports2024
A generalized model for accurate wheat spike detection and counting in complex scenarios.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Bidirectional state space modeling for lightweight and robust wheat head detection in complex agricultural environments.Scientific reports · 2026Article
- LiteMS-YOLO: a lightweight framework for small target detection in complex wheat field environments.Frontiers in plant science · 2026Article
- Transfer learning models for wheat ear detection on multi-source dataset.Scientific reports · 2025Article
- FEWheat-YOLO: A Lightweight Improved Algorithm for Wheat Spike Detection.Plants (Basel, Switzerland) · 2025Article
- LGWheatNet: A Lightweight Wheat Spike Detection Model Based on Multi-Scale Information Fusion.Plants (Basel, Switzerland) · 2025Article
- Automatic detection and counting of wheat seedling based on unmanned aerial vehicle images.Frontiers in plant science · 2025Article
- Automatic detection and counting of wheat spike based on DMseg-Count.Scientific reports · 2024Article
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Authors and funding
10 authors.
Funding
Abstract
Wheat is a crucial crop worldwide, and accurate detection and counting of wheat spikes are vital for yield estimation and breeding. However, these tasks are daunting in complex field environments. To tackle this, we introduce RIA-SpikeNet, a model designed to detect and count wheat spikes in such conditions. First, we introduce an Implicit Decoupling Detection Head to incorporate more implicit knowledge, enabling the model to better distinguish visually similar wheat spikes. Second, Asymmetric Loss is employed as the confidence loss function, enhancing the learning weights of positive and hard samples, thus improving performance in complex scenes. Lastly, the backbone network is modified through reparameterization and the use of larger convolutional kernels, expanding the effective receptive field and improving shape information extraction. These enhancements significantly improve the model's ability to detect and count wheat spikes accurately. RIA-SpikeNet outperforms the state-of-the-art YOLOv8 detection model, achieving a competitive 81.54% mAP and 90.29% R
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Registered trials
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