ArticleSensors (Basel, Switzerland)2021
Automated Counting Grains on the Rice Panicle Based on Deep Learning Method.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Fine-grained 3D rice phenotyping via multi-scale NeRF and multimodal segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- Advanced deep learning vision transformer models for intelligent grain counting in agricultural data analytics.Scientific reports · 2026Article
- LKNet: Enhancing rice canopy panicle counting accuracy with an optimized point-based framework.Plant phenomics (Washington, D.C.) · 2025Article
- A method of rice panicle number counting based on improved CSRNet model.Frontiers in plant science · 2025Article
- A field rice panicle detection model based on improved YOLOv11x.Frontiers in plant science · 2025Article
- Leaf area estimation in small-seeded broccoli using a lightweight instance segmentation framework based on improved YOLOv11-AreaNet.Frontiers in plant science · 2025Article
- Wheat Seed Detection and Counting Method Based on Improved YOLOv8 Model.Sensors (Basel, Switzerland) · 2024Article
- Maize plant height automatic reading of measurement scale based on improved YOLOv5 lightweight model.PeerJ. Computer science · 2024Article
- Size measurement and filled/unfilled detection of rice grains using backlight image processing.Frontiers in plant science · 2023Article
- An Intelligent Rice Yield Trait Evaluation System Based on Threshed Panicle Compensation.Frontiers in plant science · 2022Article
- Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
Grain number per rice panicle, which directly determines grain yield, is an important agronomic trait for rice breeding and yield-related research. However, manually counting grains of rice per panicle is time-consuming, laborious, and error-prone. In this research, a grain detection model was proposed to automatically recognize and count grains on primary branches of a rice panicle. The model used image analysis based on deep learning convolutional neural network (CNN), by integrating the feature pyramid network (FPN) into the faster R-CNN network. The performance of the grain detection model was compared to that of the original faster R-CNN model and the SSD model, and it was found that the grain detection model was more reliable and accurate. The accuracy of the grain detection model was not affected by the lighting condition in which images of rice primary branches were taken. The model worked well for all rice branches with various numbers of grains. Through applying the grain detection model to images of fresh and dry branches, it was found that the model performance was not affected by the grain moisture conditions. The overall accuracy of the grain detection model was 99.4%. Results demonstrated that the model was accurate, reliable, and suitable for detecting grains of rice panicles with various conditions.
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