Abstract
Main conclusion
This study developed the reliable Mask R-CNN model to detect stomata in Lonicera caerulea. The obtained data could be utilized for evaluating some characters such as stomatal number and aperture area.
Abstract
The native distribution of haskap (Lonicera caerulea L.), a small-shrub species, extends through Northern Eurasia, Japan, and North America. Stomatal observation is important for plant research to evaluate the physiological status and to investigate the effect of ploidy levels on phenotypes. However, manual annotation of stomata using microscope software or ImageJ is time consuming. Therefore, an efficient method to phenotype stomata is needed. In this study, we used the Mask Regional Convolutional Neural Network (Mask R-CNN), a deep learning model, to analyze the stomata of haskap efficiently and accurately. We analyzed haskap plants (dwarf and giant phenotypes) with the same ploidy but different phenotypes, including leaf area, stomatal aperture area, stomatal density, and total number of stomata. The R-square value of the estimated stomatal aperture area was 0.92 and 0.93 for the dwarf and giant plants, respectively. The R-square value of the estimated stomatal number was 0.99 and 0.98 for the two phenotypes. The results showed that the measurements obtained using the models were as accurate as the manual measurements. Statistical analysis revealed that the stomatal density of the dwarf plants was higher than that of the giant plants, but the maximum stomatal aperture area, average stomatal aperture area, total number of stomata, and average leaf area were lower than those of the giant plants. A high-precision, rapid, and large-scale detection method was developed by training the Mask R-CNN model. This model can help save time and increase the volume of data.
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Data availability
Data will be made available on request.
Program availability
The detection program of haskap stomata and the operation method protocol will be made available on request.
References
Al-Salman Y, Ghannoum O, Cano FJ (2023) Midday water use efficiency in sorghum is linked to faster stomatal closure rate, lower stomatal aperture and higher stomatal density. Plant J. https://doi.org/10.1111/tpj.16346
Aono AH, Nagai JS, Dickel GDSM, Marinho RC, de Oliveira PEAM, Papa JP, Faria FA (2021) A stomata classification and detection system in microscope images of maize cultivars. PLoS ONE 16:e0258679. https://doi.org/10.1371/journal.pone.0258679
Araújo WL, Fernie AR, Nunes-Nesi A (2011) Control of stomatal aperture: a renaissance of the old guard. Plant Signal Behav 6:1305–1311. https://doi.org/10.4161/psb.6.9.16425
Ascough GD, Van Staden J, Erwin JE (2008) Effectiveness of colchicine and oryzalin at inducing polyploidy in Watsonia lepida NE Brown. HortScience 43:2248–2251. https://doi.org/10.21273/HORTSCI.43.7.2248
Bheemanahalli R, Wang C, Bashir E, Chiluwal A, Pokharel M, Perumal R, Moghimi N, Ostmeyer T, Caragea D, Jagadish SVK (2021) Classical phenotyping and deep learning concur on genetic control of stomatal density and area in sorghum. Plant Physiol 186:1562–1579. https://doi.org/10.1093/plphys/kiab174
Caine RS, Harrison EL, Sloan J, Flis PM, Fischer S, Khan MS, Nguyen PT, Nguyen LT, Gray JE, Croft H (2023) The influences of stomatal size and density on rice abiotic stress resilience. New Phytol 237:2180–2195. https://doi.org/10.1111/nph.18704
Eng WH, Ho WS, Ling KH (2021) In vitro induction and identification of polyploid Neolamarckia cadamba plants by colchicine treatment. PeerJ 9:e12399. https://doi.org/10.7717/peerj.12399
Fujita R, Hayasaka T, Jin S, Hui SP, Hoshino Y (2020) Comparison of anthocyanin distribution in berries of Haskap (Lonicera caerulea subsp. edulis (Turcz. ex. Herder) Hultén), Miyama-uguisukagura (Lonicera gracilipes Miq.), and their interspecific hybrid using imaging mass spectrometry. Plant Sci 300:110633. https://doi.org/10.1016/j.plantsci.2020.110633
Fujita R, Jin S, Matoba K, Hoshino Y (2023) Novel production of β-cryptoxanthin in haskap (Lonicera caerulea subsp. edulis) hybrids: Improvement of carotenoid biosynthesis by interspecific hybridization. Sci Hortic. 308:111547. https://doi.org/10.1016/j.scienta.2022.111547
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In Proceedings of the IEEE international conference on computer vision, pp 2961–2969
Higaki T, Kutsuna N, Hasezawa S (2014) CARTA-based semiautomatic detection of stomatal regions on an Arabidopsis cotyledon surface. Plant Morphol 26:9–12. https://doi.org/10.5685/plmorphol.26.9
Hoshino Y (2022) An overview of the current research program for haskap (Lonicera caerulea), a useful genetic resource in Hokkaido, Japan. Eurasian J for Res 22:12–14. https://doi.org/10.14943/EJFR.22.12
Jayakody H, Petrie P, Boer HJD, Whitty M (2021) A generalised approach for high-throughput instance segmentation of stomata in microscope images. Plant Methods 17:27. https://doi.org/10.1186/s13007-021-00727-4
Khazaei H, Monneveux P, Hongbo S, Mohammady S (2010) Variation for stomatal characteristics and water use efficiency among diploid, tetraploid and hexaploid Iranian wheat landraces. Genet Resour Crop Evol 57:307–314. https://doi.org/10.1007/s10722-009-9471-x
Koutoulis A, Roy AT, Price A, Sherriff L, Leggett G (2005) DNA ploidy level of colchicine-treated hops (Humulus lupulus L.). Sci Hortic 105:263–268. https://doi.org/10.1016/j.scienta.2005.01.010
Li X, Guo S, Gong L, Lan Y (2023) An automatic plant leaf stoma detection method based on YOLOv5. IET Image Process 17:67–76. https://doi.org/10.1049/ipr2.12617
Liang X, Xu X, Wang Z, He L, Zhang K, Liang B, Ye J, Shi J, Wu X, Dai M, Yang W (2022) StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model. Plant Biotechnol J 20:577–591. https://doi.org/10.1111/pbi.13741
Limin Y, Mei H, Guangsheng Z, Jiandong L (2007) The changes in water-use efficiency and stoma density of Leymus chinensis along Northeast China transect. Acta Ecol Sin 27:16–23. https://doi.org/10.1016/S1872-2032(07)60006-7
Marinho RC, Mendes-Rodrigues C, Bonetti AM, Oliveira PE (2014) Pollen and stomata morphometrics and polyploidy in Eriotheca (Malvaceae-Bombacoideae). Plant Biol (Stuttg) 16:508–511. https://doi.org/10.1111/plb.12135
Melotto M, Underwood W, He SY (2008) Role of stomata in plant innate immunity and foliar bacterial diseases. Annu Rev Phytopathol 46:101–122. https://doi.org/10.1146/annurev.phyto.121107.104959
Millstead L, Jayakody H, Patel H, Kaura V, Petrie PR, Tomasetig F, Whitty M (2020) Accelerating automated stomata analysis through simplified sample collection and imaging techniques. Front Plant Sci 11:580389
Mishra MK (1997) Stomatal characteristics at different ploidy levels in Coffea L. Ann Bot 80:689–692. https://doi.org/10.1006/anbo.1997.0491
Miyashita T, Hoshino Y (2010) Interspecific hybridization in Lonicera caerulea and Lonicera gracilipes: the occurrence of green/albino plants by reciprocal crossing. Sci Hortic 125:692–699. https://doi.org/10.1016/j.scienta.2010.05.032
Miyashita T, Hoshino Y (2015) Interploid and intraploid hybridizations to produce polyploid Haskap (Lonicera caerulea var. emphyllocalyx) plants. Euphytica 201:15–27. https://doi.org/10.1007/s10681-014-1159-4
Miyashita T, Ohashi T, Shibata F, Araki H, Hoshino Y (2009) Plant regeneration with maintenance of the endosperm ploidy level by endosperm culture in Lonicera caerulea var. emphyllocalyx. Plant Cell Tiss Organ Cult 98:291–301. https://doi.org/10.1007/s11240-009-9562-6
Miyashita T, Araki H, Hoshino Y (2011) Ploidy distribution and DNA content variations of Lonicera caerulea (Caprifoliaceae) in Japan. J Plant Res 124:1–9. https://doi.org/10.1007/s10265-010-0341-6
Padoan D, Mossad A, Chiancone B, Germana MA, Khan PSSV (2013) Ploidy levels in Citrus clementine affects leaf morphology, stomatal density and water content. Theor Exp Plant Physiol 25:283–290
Pathoumthong P, Zhang Z, Roy SJ, El Habti A (2023) Rapid non-destructive method to phenotype stomatal traits. Plant Methods 19:1–9. https://doi.org/10.1186/s13007-023-01016-y
Sakata N, Ino T, Hayashi C, Ishiga T, Ishiga Y (2023) Controlling stomatal aperture, a potential strategy for managing plant bacterial disease. Plant Sci 327:111534. https://doi.org/10.1016/j.plantsci.2022.111534
Schindelin J, Rueden CT, Hiner MC, Eliceiri KW (2015) The ImageJ ecosystem: an open platform for biomedical image analysis. Mol Reprod Dev 82:518–529. https://doi.org/10.1002/mrd.22489
Song W, Li J, Li K, Chen J, Huang J (2020) An automatic method for stomatal pore detection and measurement in microscope images of plant leaf based on a convolutional neural network model. Forests 11:954. https://doi.org/10.3390/f11090954
Thompson MM (2006) Introducing haskap, Japanese blue honeysuckle. J Am Pomol Soc 60:164
Weyers JD, Johansen LG (1985) Accurate estimation of stomatal aperture from silicone rubber impressions. New Phytol 101:109–115
Acknowledgements
This work was partially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 20K06028. The authors are grateful to H. Nakano for the maintenance of the experimental materials.
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Communicated by Anastasios Melis.
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Meng, X., Nakano, A. & Hoshino, Y. Automated estimation of stomatal number and aperture in haskap (Lonicera caerulea L.). Planta 258, 77 (2023). https://doi.org/10.1007/s00425-023-04231-y
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DOI: https://doi.org/10.1007/s00425-023-04231-y