ArticlePlant methods2019
Image analysis-based recognition and quantification of grain number per panicle in rice.
Article in Plant methods, 2019. 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.
- Study on automatic detection of wheat spike grain number based on deep learning.Frontiers in plant science · 2026Article
- A method of rice panicle number counting based on improved CSRNet model.Frontiers in plant science · 2025Article
- Deep learning-based methods for phenotypic trait extraction in rice panicles.Frontiers in plant science · 2025Article
- A field rice panicle detection model based on improved YOLOv11x.Frontiers in plant science · 2025Article
- Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision.Plant methods · 2024Article
- SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits.Plant cell reports · 2024Article
- Wheat Seed Detection and Counting Method Based on Improved YOLOv8 Model.Sensors (Basel, Switzerland) · 2024Article
- Genetic analysis of cassava brown streak disease root necrosis using image analysis and genome-wide association studies.Frontiers in plant science · 2024Article
- PanicleNeRF: Low-Cost, High-Precision In-Field Phenotyping of Rice Panicles with Smartphone.Plant phenomics (Washington, D.C.) · 2024Article
- A High-Throughput Method for Accurate Extraction of Intact Rice Panicle Traits.Plant phenomics (Washington, D.C.) · 2024Article
- Deciphering individual triticale grain weight patterns: A gaussian mixture model approach.PloS one · 2024Article
- Rice Grain Detection and Counting Method Based on TCLE-YOLO Model.Sensors (Basel, Switzerland) · 2023Article
- YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting.Plant methods · 2023Article
- High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning.Frontiers in plant science · 2023Article
- From Prototype to Inference: A Pipeline to Apply Deep Learning in Sorghum Panicle Detection.Plant phenomics (Washington, D.C.) · 2023Article
- An Intelligent Rice Yield Trait Evaluation System Based on Threshed Panicle Compensation.Frontiers in plant science · 2022Article
- Exploring Soybean Flower and Pod Variation Patterns During Reproductive Period Based on Fusion Deep Learning.Frontiers in plant science · 2022Article
- Artificial intelligence: A powerful paradigm for scientific research.Innovation (Cambridge (Mass.)) · 2021Review
- Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies.Sensors (Basel, Switzerland) · 2021Article
- Automated Counting Grains on the Rice Panicle Based on Deep Learning Method.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe number grain per panicle of rice is an important phenotypic trait and a significant index for variety screening and cultivation management. The methods that are currently used to count the number of grains per panicle are manually conducted, making them labor intensive and time consuming. Existing image-based grain counting methods had difficulty in separating overlapped grains.
resultsIn this study, we aimed to develop an image analysis-based method to quickly quantify the number of rice grains per panicle. We compared the counting accuracy of several methods among different image acquisition devices and multiple panicle shapes on both
conclusionsWe developed a rice grain counting method that accurately counts the number of grains on a detached panicle, and believe this method can be a huge asset for guiding the development of high throughput methods for counting the grain number per panicle in other crops.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.