ArticleFrontiers in plant science2022
Field rice panicle detection and counting based on deep learning.
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 22 papers.
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Who cites it
22 citing papers in PubMed.
- Plant counting: origins, development, and future paths.Plant phenomics (Washington, D.C.) · 2026Review
- OPAL-Flow: Orientation-aware rice panicle detection and minute-scale anthesis rhythm identification under field conditions.Plant phenomics (Washington, D.C.) · 2026Article
- High-density field-based 3D reconstruction of rice architecture across diverse cultivars for genome-wide association studies.Plant methods · 2026Article
- Rice spikelet flowering-state detection based on an improved YOLOv11n model.Frontiers in plant science · 2026Article
- UAV-Based Automatic Detection of Missing Rice Seedlings Using the PCERT-DETR Model.Plants (Basel, Switzerland) · 2025Article
- Pod-pose : an efficient top-down keypoint detection model for fine-grained pod phenotyping in mature soybean.Plant methods · 2025Article
- Deep learning-based semantic segmentation for rice yield estimation by analyzing the dynamic change of panicle coverage.Frontiers in plant science · 2025Article
- An android-smartphone application for rice panicle detection and rice growth stage recognition using a lightweight YOLO network.Frontiers in plant science · 2025Article
- A field rice panicle detection model based on improved YOLOv11x.Frontiers in plant science · 2025Article
- Lightweight Corn Leaf Detection and Counting Using Improved YOLOv8.Sensors (Basel, Switzerland) · 2024Article
- Multi-Scale Attention Network for Vertical Seed Distribution in Soybean Breeding Fields.Plant phenomics (Washington, D.C.) · 2024Article
- An Overview of High-Throughput Crop Phenotyping: Platform, Image Analysis, Data Mining, and Data Management.Methods in molecular biology (Clifton, N.J.) · 2024Review
- High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits.Frontiers in plant science · 2024Article
- Comprehensive dataset of annotated rice panicle image from Bangladesh.Data in brief · 2023Article
- Rice Grain Detection and Counting Method Based on TCLE-YOLO Model.Sensors (Basel, Switzerland) · 2023Article
- A CNN-LSTM-att hybrid model for classification and evaluation of growth status under drought and heat stress in chinese fir (Cunninghamia lanceolata).Plant methods · 2023Article
- Analyzing Nitrogen Effects on Rice Panicle Development by Panicle Detection and Time-Series Tracking.Plant phenomics (Washington, D.C.) · 2023Article
- Panicle-Cloud: An Open and AI-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice.Plant phenomics (Washington, D.C.) · 2023Article
- Maize seed appearance quality assessment based on improved Inception-ResNet.Frontiers in plant science · 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
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8 authors.
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Abstract
Panicle number is directly related to rice yield, so panicle detection and counting has always been one of the most important scientific research topics. Panicle counting is a challenging task due to many factors such as high density, high occlusion, and large variation in size, shape, posture et.al. Deep learning provides state-of-the-art performance in object detection and counting. Generally, the large images need to be resized to fit for the video memory. However, small panicles would be missed if the image size of the original field rice image is extremely large. In this paper, we proposed a rice panicle detection and counting method based on deep learning which was especially designed for detecting rice panicles in rice field images with large image size. Different object detectors were compared and YOLOv5 was selected with MAPE of 3.44% and accuracy of 92.77%. Specifically, we proposed a new method for removing repeated detections and proved that the method outperformed the existing NMS methods. The proposed method was proved to be robust and accurate for counting panicles in field rice images of different illumination, rice accessions, and image input size. Also, the proposed method performed well on UAV images. In addition, an open-access and user-friendly web portal was developed for rice researchers to use the proposed method conveniently.
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