DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field. Jiang, Y., Li, C., Xu, R., Sun, S., Robertson, J. S., & Paterson, A. H. Plant Methods, 2020. Pb7vw Times Cited:4 Cited References Count:24![DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex Background Flowering is one of the most important processes for flowering plants such as cotton, reflecting the transition from vegetative to reproductive growth and is of central importance to crop yield and adaptability. Conventionally, categorical scoring systems have been widely used to study flowering patterns, which are laborious and subjective to apply. The goal of this study was to develop a deep learning-based approach to characterize flowering patterns for cotton plants that flower progressively over several weeks, with flowers distributed across much of the plant. Results A ground mobile system (GPhenoVision) was modified with a multi-view color imaging module, to acquire images of a plant from four viewing angles at a time. A total of 116 plants from 23 genotypes were imaged during an approximately 2-month period with an average scanning interval of 2-3 days, yielding a dataset containing 8666 images. A subset (475) of the images were randomly selected and manually annotated to form datasets for training and selecting the best object detection model. With the best model, a deep learning-based approach (DeepFlower) was developed to detect and count individual emerging blooms for a plant on a given date. The DeepFlower was used to process all images to obtain bloom counts for individual plants over the flowering period, using the resulting counts to derive flowering curves (and thus flowering characteristics). Regression analyses showed that the DeepFlower method could accurately (R-2 = 0.88 and RMSE = 0.79) detect and count emerging blooms on cotton plants, and statistical analyses showed that imaging-derived flowering characteristics had similar effectiveness as manual assessment for identifying differences among genetic categories or genotypes. Conclusions The developed approach could thus be an effective and efficient tool to characterize flowering patterns for flowering plants (such as cotton) with complex canopy architecture.
@article{RN35,
author = {Jiang, Yu and Li, Changying and Xu, Rui and Sun, Shangpeng and Robertson, Jon S. and Paterson, Andrew H.},
title = {DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field},
journal = {Plant Methods},
volume = {16},
number = {1},
note = {Pb7vw
Times Cited:4
Cited References Count:24},
abstract = {Background Flowering is one of the most important processes for flowering plants such as cotton, reflecting the transition from vegetative to reproductive growth and is of central importance to crop yield and adaptability. Conventionally, categorical scoring systems have been widely used to study flowering patterns, which are laborious and subjective to apply. The goal of this study was to develop a deep learning-based approach to characterize flowering patterns for cotton plants that flower progressively over several weeks, with flowers distributed across much of the plant. Results A ground mobile system (GPhenoVision) was modified with a multi-view color imaging module, to acquire images of a plant from four viewing angles at a time. A total of 116 plants from 23 genotypes were imaged during an approximately 2-month period with an average scanning interval of 2-3 days, yielding a dataset containing 8666 images. A subset (475) of the images were randomly selected and manually annotated to form datasets for training and selecting the best object detection model. With the best model, a deep learning-based approach (DeepFlower) was developed to detect and count individual emerging blooms for a plant on a given date. The DeepFlower was used to process all images to obtain bloom counts for individual plants over the flowering period, using the resulting counts to derive flowering curves (and thus flowering characteristics). Regression analyses showed that the DeepFlower method could accurately (R-2 = 0.88 and RMSE = 0.79) detect and count emerging blooms on cotton plants, and statistical analyses showed that imaging-derived flowering characteristics had similar effectiveness as manual assessment for identifying differences among genetic categories or genotypes. Conclusions The developed approach could thus be an effective and efficient tool to characterize flowering patterns for flowering plants (such as cotton) with complex canopy architecture.},
keywords = {flowering pattern
deep learning
object detection
high-throughput plant phenotyping
image analysis
time
association
genes},
DOI = {https://doi.org/10.1186/s13007-020-00698-y},
url = {https://plantmethods.biomedcentral.com/track/pdf/10.1186/s13007-020-00698-y.pdf},
year = {2020},
type = {Journal Article}
}
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H."],"bibdata":{"bibtype":"article","type":"Journal Article","author":[{"propositions":[],"lastnames":["Jiang"],"firstnames":["Yu"],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Changying"],"suffixes":[]},{"propositions":[],"lastnames":["Xu"],"firstnames":["Rui"],"suffixes":[]},{"propositions":[],"lastnames":["Sun"],"firstnames":["Shangpeng"],"suffixes":[]},{"propositions":[],"lastnames":["Robertson"],"firstnames":["Jon","S."],"suffixes":[]},{"propositions":[],"lastnames":["Paterson"],"firstnames":["Andrew","H."],"suffixes":[]}],"title":"DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field","journal":"Plant Methods","volume":"16","number":"1","note":"Pb7vw Times Cited:4 Cited References Count:24","abstract":"Background Flowering is one of the most important processes for flowering plants such as cotton, reflecting the transition from vegetative to reproductive growth and is of central importance to crop yield and adaptability. Conventionally, categorical scoring systems have been widely used to study flowering patterns, which are laborious and subjective to apply. The goal of this study was to develop a deep learning-based approach to characterize flowering patterns for cotton plants that flower progressively over several weeks, with flowers distributed across much of the plant. Results A ground mobile system (GPhenoVision) was modified with a multi-view color imaging module, to acquire images of a plant from four viewing angles at a time. A total of 116 plants from 23 genotypes were imaged during an approximately 2-month period with an average scanning interval of 2-3 days, yielding a dataset containing 8666 images. A subset (475) of the images were randomly selected and manually annotated to form datasets for training and selecting the best object detection model. With the best model, a deep learning-based approach (DeepFlower) was developed to detect and count individual emerging blooms for a plant on a given date. The DeepFlower was used to process all images to obtain bloom counts for individual plants over the flowering period, using the resulting counts to derive flowering curves (and thus flowering characteristics). Regression analyses showed that the DeepFlower method could accurately (R-2 = 0.88 and RMSE = 0.79) detect and count emerging blooms on cotton plants, and statistical analyses showed that imaging-derived flowering characteristics had similar effectiveness as manual assessment for identifying differences among genetic categories or genotypes. Conclusions The developed approach could thus be an effective and efficient tool to characterize flowering patterns for flowering plants (such as cotton) with complex canopy architecture.","keywords":"flowering pattern deep learning object detection high-throughput plant phenotyping image analysis time association genes","doi":"https://doi.org/10.1186/s13007-020-00698-y","url":"https://plantmethods.biomedcentral.com/track/pdf/10.1186/s13007-020-00698-y.pdf","year":"2020","bibtex":"@article{RN35,\r\n author = {Jiang, Yu and Li, Changying and Xu, Rui and Sun, Shangpeng and Robertson, Jon S. and Paterson, Andrew H.},\r\n title = {DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field},\r\n journal = {Plant Methods},\r\n volume = {16},\r\n number = {1},\r\n note = {Pb7vw\r\nTimes Cited:4\r\nCited References Count:24},\r\n abstract = {Background Flowering is one of the most important processes for flowering plants such as cotton, reflecting the transition from vegetative to reproductive growth and is of central importance to crop yield and adaptability. Conventionally, categorical scoring systems have been widely used to study flowering patterns, which are laborious and subjective to apply. The goal of this study was to develop a deep learning-based approach to characterize flowering patterns for cotton plants that flower progressively over several weeks, with flowers distributed across much of the plant. Results A ground mobile system (GPhenoVision) was modified with a multi-view color imaging module, to acquire images of a plant from four viewing angles at a time. A total of 116 plants from 23 genotypes were imaged during an approximately 2-month period with an average scanning interval of 2-3 days, yielding a dataset containing 8666 images. A subset (475) of the images were randomly selected and manually annotated to form datasets for training and selecting the best object detection model. With the best model, a deep learning-based approach (DeepFlower) was developed to detect and count individual emerging blooms for a plant on a given date. The DeepFlower was used to process all images to obtain bloom counts for individual plants over the flowering period, using the resulting counts to derive flowering curves (and thus flowering characteristics). Regression analyses showed that the DeepFlower method could accurately (R-2 = 0.88 and RMSE = 0.79) detect and count emerging blooms on cotton plants, and statistical analyses showed that imaging-derived flowering characteristics had similar effectiveness as manual assessment for identifying differences among genetic categories or genotypes. Conclusions The developed approach could thus be an effective and efficient tool to characterize flowering patterns for flowering plants (such as cotton) with complex canopy architecture.},\r\n keywords = {flowering pattern\r\ndeep learning\r\nobject detection\r\nhigh-throughput plant phenotyping\r\nimage analysis\r\ntime\r\nassociation\r\ngenes},\r\n DOI = {https://doi.org/10.1186/s13007-020-00698-y},\r\n url = {https://plantmethods.biomedcentral.com/track/pdf/10.1186/s13007-020-00698-y.pdf},\r\n year = {2020},\r\n type = {Journal Article}\r\n}\r\n\r\n","author_short":["Jiang, Y.","Li, C.","Xu, R.","Sun, S.","Robertson, J. S.","Paterson, A. 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