ArticlePlant physiology2019
Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.
Article in Plant physiology, 2019. 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
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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.
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Who cites it
7 citing papers in PubMed, 26 citations in OpenAlex.
- Bidirectional state space modeling for lightweight and robust wheat head detection in complex agricultural environments.Scientific reports · 2026Article
- Transfer learning models for wheat ear detection on multi-source dataset.Scientific reports · 2025Article
- Application of deep learning for the analysis of stomata: a review of current methods and future directions.Journal of experimental botany · 2024Review
- A 'wiring diagram' for source strength traits impacting wheat yield potential.Journal of experimental botany · 2023Review
- Deep learning-based high-throughput phenotyping can drive future discoveries in plant reproductive biology.Plant reproduction · 2021Article
- Photosynthetic Acclimation to Fluctuating Irradiance in Plants.Frontiers in plant science · 2020Review
- Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods.Plant phenomics (Washington, D.C.) · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in most agricultural systems, yet the complexity of the field environment means that it remained understudied. Despite the ready availability of image sequences showing plant motion, the cultivation of crop plants in dense field stands makes it difficult to detect features and characterize their general movement traits. Here, we present a robust method for characterizing motion in field-grown wheat plants (
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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.