ArticleFrontiers in plant science2021
Occlusion Robust Wheat Ear Counting Algorithm Based on Deep Learning.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
- Wheat spike and spikelet detection and counting from high-resolution digital imagery using YOLO with Oriented Bounding Boxes.Scientific reports · 2026Article
- Efficient and accurate tiller counting of hand-collected samples using images of straw bundles.MethodsX · 2026Article
- Deep learning-based high-throughput phenotyping for tiller quantification in interspecific bentgrass hybrids using YOLOv8.Frontiers in plant science · 2026Article
- Transfer learning models for wheat ear detection on multi-source dataset.Scientific reports · 2025Article
- Lightweight deep neural network for contour detection and extraction of wheat spikes in complex field environments.Plant methods · 2025Article
- Research on detection and counting method of wheat ears in the field based on YOLOv11-EDS.Frontiers in plant science · 2025Article
- CropPhenoX: high-throughput automatic extraction system for wheat seedling phenotypic traits based on software and hardware collaboration.Frontiers in plant science · 2025Article
- 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
- Oriented feature pyramid network for small and dense wheat heads detection and counting.Scientific reports · 2024Article
- Wheat Seed Detection and Counting Method Based on Improved YOLOv8 Model.Sensors (Basel, Switzerland) · 2024Article
- A method for small-sized wheat seedlings detection: from annotation mode to model construction.Plant methods · 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
- Feature diffusion reconstruction mechanism network for crop spike head detection.Frontiers in plant science · 2024Article
- FIDMT-GhostNet: a lightweight density estimation model for wheat ear counting.Frontiers in plant science · 2024Article
- MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing.Frontiers in plant science · 2024Article
- WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network.Plant methods · 2023Article
- Segmentation and counting of wheat spike grains based on deep learning and textural feature.Plant methods · 2023Article
- Improving multi-scale detection layers in the deep learning network for wheat spike detection based on interpretive analysis.Plant methods · 2023Article
- Automatic rape flower cluster counting method based on low-cost labelling and UAV-RGB images.Plant methods · 2023Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Counting the number of wheat ears in images under natural light is an important way to evaluate the crop yield, thus, it is of great significance to modern intelligent agriculture. However, the distribution of wheat ears is dense, so the occlusion and overlap problem appears in almost every wheat image. It is difficult for traditional image processing methods to solve occlusion problem due to the deficiency of high-level semantic features, while existing deep learning based counting methods did not solve the occlusion efficiently. This article proposes an improved EfficientDet-D0 object detection model for wheat ear counting, and focuses on solving occlusion. First, the transfer learning method is employed in the pre-training of the model backbone network to extract the high-level semantic features of wheat ears. Secondly, an image augmentation method Random-Cutout is proposed, in which some rectangles are selected and erased according to the number and size of the wheat ears in the images to simulate occlusion in real wheat images. Finally, convolutional block attention module (CBAM) is adopted into the EfficientDet-D0 model after the backbone, which makes the model refine the features, pay more attention to the wheat ears and suppress other useless background information. Extensive experiments are done by feeding the features to detection layer, showing that the counting accuracy of the improved EfficientDet-D0 model reaches 94%, which is about 2% higher than the original model, and false detection rate is 5.8%, which is the lowest among comparative methods.
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