ArticlePlant methods2015
Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images.
Article in Plant methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- BloomSight: An ultra-high-frequency phenotyping framework for diurnal flowering dynamics inPlant phenomics (Washington, D.C.) · 2026Article
- OPAL-Flow: Orientation-aware rice panicle detection and minute-scale anthesis rhythm identification under field conditions.Plant phenomics (Washington, D.C.) · 2026Article
- Rice spikelet flowering-state detection based on an improved YOLOv11n model.Frontiers in plant science · 2026Article
- Machine learning assisted analysis of rice flower opening times using a low-cost time-lapse camera.Journal of plant research · 2025Article
- The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset.Plant phenomics (Washington, D.C.) · 2025Article
- A Lightweight Recognition Method for Rice Growth Period Based on Improved YOLOv5s.Sensors (Basel, Switzerland) · 2023Article
- Field phenotyping for African crops: overview and perspectives.Frontiers in plant science · 2023Review
- Analyzing Nitrogen Effects on Rice Panicle Development by Panicle Detection and Time-Series Tracking.Plant phenomics (Washington, D.C.) · 2023Article
- A Strategy for the Acquisition and Analysis of Image-Based Phenome in Rice during the Whole Growth Period.Plant phenomics (Washington, D.C.) · 2023Article
- Image-Based Phenotyping for Non-Destructive In Situ Rice (Sensors (Basel, Switzerland) · 2022Article
- High-throughput field crop phenotyping: current status and challenges.Breeding science · 2022Article
- Tackling microbial threats in agriculture with integrative imaging and computational approaches.Computational and structural biotechnology journal · 2021Review
- Phenotyping Flowering in Canola (Frontiers in plant science · 2021Article
- Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography.Annals of botany · 2020Article
- Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm.Plants (Basel, Switzerland) · 2020Review
- Appraisal of wheat genomics for gene discovery and breeding applications: a special emphasis on advances in Asia.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Review
- Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops.Sensors (Basel, Switzerland) · 2020Article
- Maize tassels detection: a benchmark of the state of the art.Plant methods · 2020Article
- Unsupervised Bayesian learning for rice panicle segmentation with UAV images.Plant methods · 2020Article
- TasselNetV2+: A Fast Implementation for High-Throughput Plant Counting From High-Resolution RGB Imagery.Frontiers in plant science · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFlowering (spikelet anthesis) is one of the most important phenotypic characteristics of paddy rice, and researchers expend efforts to observe flowering timing. Observing flowering is very time-consuming and labor-intensive, because it is still visually performed by humans. An image-based method that automatically detects the flowering of paddy rice is highly desirable. However, varying illumination, diversity of appearance of the flowering parts of the panicles, shape deformation, partial occlusion, and complex background make the development of such a method challenging.
resultsWe developed a method for detecting flowering panicles of rice in RGB images using scale-invariant feature transform descriptors, bag of visual words, and a machine learning method, support vector machine. Applying the method to time-series images, we estimated the number of flowering panicles and the diurnal peak of flowering on each day. The method accurately detected the flowering parts of panicles during the flowering period and quantified the daily and diurnal flowering pattern.
conclusionsA powerful method for automatically detecting flowering panicles of paddy rice in time-series RGB images taken under natural field conditions is described. The method can automatically count flowering panicles. In application to time-series images, the proposed method can well quantify the daily amount and the diurnal changes of flowering during the flowering period and identify daily peaks of flowering.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.