ArticleSensors (Basel, Switzerland)2020
Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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
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Who cites it
10 citing papers in PubMed.
- A panomics-driven framework for the improvement of major food legume crops: advances, challenges, and future prospects.Horticulture research · 2025Article
- Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies.Plants (Basel, Switzerland) · 2024Review
- A comprehensive characterization of agronomic and end-use quality phenotypes across a quinoa world core collection.Frontiers in plant science · 2023Article
- Use of an unmanned aerial vehicle for monitoring and prediction of oilseed rape crop performance.PloS one · 2023Article
- Dissecting the Genetic Structure of Maize Leaf Sheaths at Seedling Stage by Image-Based High-Throughput Phenotypic Acquisition and Characterization.Frontiers in plant science · 2022Article
- Genomics Associated Interventions for Heat Stress Tolerance in Cool Season Adapted Grain Legumes.International journal of molecular sciences · 2021Review
- Omics for the Improvement of Abiotic, Biotic, and Agronomic Traits in Major Cereal Crops: Applications, Challenges, and Prospects.Plants (Basel, Switzerland) · 2021Review
- High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops.International journal of molecular sciences · 2021Review
- Crop Performance Evaluation of Chickpea and Dry Pea Breeding Lines Across Seasons and Locations Using Phenomics Data.Frontiers in plant science · 2021Article
- Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, the standard protocol used for phenotyping flowering, is a low-throughput and subjective method. In this study, we evaluated multiple imaging sensors (RGB and multiple multispectral cameras), image resolution (proximal/remote sensing at 1.6 to 30 m above ground level/AGL), and image processing (standard and unsupervised learning) techniques in monitoring flowering intensity of four cool-season crops (canola, camelina, chickpea, and pea) to enhance the accuracy and efficiency in quantifying flowering traits. The features (flower area, percentage of flower area with respect to canopy area) extracted from proximal (1.6-2.2 m AGL) RGB and multispectral (with near infrared, green and blue band) image data were strongly correlated (
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Registered trials
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