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Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops

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 pr...

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Autores principales: Zhang, Chongyuan, Craine, Wilson A., McGee, Rebecca J., Vandemark, George J., Davis, James B., Brown, Jack, Hulbert, Scot H., Sankaran, Sindhuja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085647/
https://www.ncbi.nlm.nih.gov/pubmed/32155830
http://dx.doi.org/10.3390/s20051450
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author Zhang, Chongyuan
Craine, Wilson A.
McGee, Rebecca J.
Vandemark, George J.
Davis, James B.
Brown, Jack
Hulbert, Scot H.
Sankaran, Sindhuja
author_facet Zhang, Chongyuan
Craine, Wilson A.
McGee, Rebecca J.
Vandemark, George J.
Davis, James B.
Brown, Jack
Hulbert, Scot H.
Sankaran, Sindhuja
author_sort Zhang, Chongyuan
collection PubMed
description 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 (r up to 0.89) with visual rating scores, especially in pea and canola. The features extracted from unmanned aerial vehicle integrated RGB image data (15–30 m AGL) could also accurately detect and quantify large flowers of winter canola (r up to 0.84), spring canola (r up to 0.72), and pea (r up to 0.72), but not camelina or chickpea flowers. When standard image processing using thresholds and unsupervised machine learning such as k-means clustering were utilized for flower detection and feature extraction, the results were comparable. In general, for applicability of imaging for flower detection, it is recommended that the image data resolution (i.e., ground sampling distance) is at least 2–3 times smaller than that of the flower size. Overall, this study demonstrates the feasibility of utilizing imaging for monitoring flowering intensity in multiple varieties of evaluated crops.
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spelling pubmed-70856472020-04-21 Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops Zhang, Chongyuan Craine, Wilson A. McGee, Rebecca J. Vandemark, George J. Davis, James B. Brown, Jack Hulbert, Scot H. Sankaran, Sindhuja Sensors (Basel) Article 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 (r up to 0.89) with visual rating scores, especially in pea and canola. The features extracted from unmanned aerial vehicle integrated RGB image data (15–30 m AGL) could also accurately detect and quantify large flowers of winter canola (r up to 0.84), spring canola (r up to 0.72), and pea (r up to 0.72), but not camelina or chickpea flowers. When standard image processing using thresholds and unsupervised machine learning such as k-means clustering were utilized for flower detection and feature extraction, the results were comparable. In general, for applicability of imaging for flower detection, it is recommended that the image data resolution (i.e., ground sampling distance) is at least 2–3 times smaller than that of the flower size. Overall, this study demonstrates the feasibility of utilizing imaging for monitoring flowering intensity in multiple varieties of evaluated crops. MDPI 2020-03-06 /pmc/articles/PMC7085647/ /pubmed/32155830 http://dx.doi.org/10.3390/s20051450 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Chongyuan
Craine, Wilson A.
McGee, Rebecca J.
Vandemark, George J.
Davis, James B.
Brown, Jack
Hulbert, Scot H.
Sankaran, Sindhuja
Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops
title Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops
title_full Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops
title_fullStr Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops
title_full_unstemmed Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops
title_short Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops
title_sort image-based phenotyping of flowering intensity in cool-season crops
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085647/
https://www.ncbi.nlm.nih.gov/pubmed/32155830
http://dx.doi.org/10.3390/s20051450
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