ArticleFrontiers in plant science2020
Assessment of Mixed Sward Using Context Sensitive Convolutional Neural Networks.
Article in Frontiers in plant science, 2020. 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.
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.
- Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning.Frontiers in plant science · 2024Article
- Tracking Ideal Varieties and Cropping Techniques for Agroecological Weed Management: A Simulation-Based Study on Pea.Frontiers in plant science · 2022Article
- Effect of different organic acid additives on the fermentation quality and bacterial community of paper mulberry (Frontiers in microbiology · 2022Article
- Editorial: Convolutional neural networks and deep learning for crop improvement and production.Frontiers in plant science · 2022Article
- Convolutional Neural Network Models Help Effectively Estimate Legume Coverage in Grass-Legume Mixed Swards.Frontiers in plant science · 2021Article
- Robust Species Distribution Mapping of Crop Mixtures Using Color Images and Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2020Article
- Identification of Weeds Based on Hyperspectral Imaging and Machine Learning.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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breeding higher yielding forage species is limited by current manual harvesting and visual scoring techniques used for measuring or estimation of biomass. Automation and remote sensing for high throughput phenotyping has been used in recent years as a viable solution to this bottleneck. Here, we focus on using RGB imaging and deep learning for white clover (
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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.