ArticlePlant phenomics (Washington, D.C.)2023
Analyzing Nitrogen Effects on Rice Panicle Development by Panicle Detection and Time-Series Tracking.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- OPAL-Flow: Orientation-aware rice panicle detection and minute-scale anthesis rhythm identification under field conditions.Plant phenomics (Washington, D.C.) · 2026Article
- CVRP: A rice image dataset with high-quality annotations for image segmentation and plant phenomics research.Plant phenomics (Washington, D.C.) · 2025Article
- StatFaRmer: cultivating insights with an advanced R shiny dashboard for digital phenotyping data analysis.Frontiers in plant science · 2025Article
- A deep learning approach for deriving wheat phenology from near-surface RGB image series using spatiotemporal fusion.Plant methods · 2024Article
- High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits.Frontiers in plant science · 2024Article
- Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming.Frontiers in plant science · 2024Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detailed observation of the phenotypic changes in rice panicle substantially helps us to understand the yield formation. In recent studies, phenotyping of rice panicles during the heading-flowering stage still lacks comprehensive analysis, especially of panicle development under different nitrogen treatments. In this work, we proposed a pipeline to automatically acquire the detailed panicle traits based on time-series images by using the YOLO v5, ResNet50, and DeepSORT models. Combined with field observation data, the proposed method was used to test whether it has an ability to identify subtle differences in panicle developments under different nitrogen treatments. The result shows that panicle counting throughout the heading-flowering stage achieved high accuracy (
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.