{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:48:38Z","timestamp":1753890518914,"version":"3.41.2"},"reference-count":65,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:p><jats:italic>Tradescantia<\/jats:italic>plant is a complex system that is sensible to environmental factors such as water supply, pH, temperature, light, radiation, impurities, and nutrient availability. It can be used as a biomonitor for environmental changes; however, the bioassays are time-consuming and have a strong human interference factor that might change the result depending on who is performing the analysis. We have developed computer vision models to study color variations from<jats:italic>Tradescantia<\/jats:italic>clone 4430 plant stamen hair cells, which can be stressed due to air pollution and soil contamination. The study introduces a novel dataset, Trad-204, comprising single-cell images from<jats:italic>Tradescantia<\/jats:italic>clone 4430, captured during the<jats:italic>Tradescantia<\/jats:italic>stamen-hair mutation bioassay (Trad-SHM). The dataset contain images from two experiments, one focusing on air pollution by particulate matter and another based on soil contaminated by diesel oil. Both experiments were carried out in Curitiba, Brazil, between 2020 and 2023. The images represent single cells with different shapes, sizes, and colors, reflecting the plant's responses to environmental stressors. An automatic classification task was developed to distinguishing between blue and pink cells, and the study explores both a baseline model and three artificial neural network (ANN) architectures, namely, TinyVGG, VGG-16, and ResNet34.<jats:italic>Tradescantia<\/jats:italic>revealed sensibility to both air particulate matter concentration and diesel oil in soil. The results indicate that Residual Network architecture outperforms the other models in terms of accuracy on both training and testing sets. The dataset and findings contribute to the understanding of plant cell responses to environmental stress and provide valuable resources for further research in automated image analysis of plant cells. Discussion highlights the impact of turgor pressure on cell shape and the potential implications for plant physiology. The comparison between ANN architectures aligns with previous research, emphasizing the superior performance of ResNet models in image classification tasks. Artificial intelligence identification of pink cells improves the counting accuracy, thus avoiding human errors due to different color perceptions, fatigue, or inattention, in addition to facilitating and speeding up the analysis process. Overall, the study offers insights into plant cell dynamics and provides a foundation for future investigations like cells morphology change. This research corroborates that biomonitoring should be considered as an important tool for political actions, being a relevant issue in risk assessment and the development of new public policies relating to the environment.<\/jats:p>","DOI":"10.3389\/fdata.2024.1384240","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T05:12:41Z","timestamp":1715749961000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Tradescantia response to air and soil pollution, stamen hair cells dataset and ANN color classification"],"prefix":"10.3389","volume":"7","author":[{"given":"Leatrice Talita","family":"Rodrigues","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Barbara Sanches Antunes","family":"Goeldner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Em\u00edlio Graciliano Ferreira","family":"Mercuri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steffen Manfred","family":"Noe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"196","DOI":"10.26599\/BDMA.2020.9020004","article-title":"Gradient amplification: An efficient way to train deep neural networks","volume":"3","author":"Basodi","year":"2020","journal-title":"Big Data Mining Analyt"},{"key":"B2","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw"},{"key":"B3","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.mrgentox.2017.09.004","article-title":"Metal bioaccumulation and mutagenesis in a tradescantia clone following long-term exposure to soils from urban industrial areas and closed landfills","volume":"823","author":"\u010c\u0117snien\u0117","year":"2017","journal-title":"Mutat. Res.\/Genet. Toxicol. Environm. Mutagen"},{"key":"B4","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1002\/cyto.a.23863","article-title":"Evaluation of deep learning strategies for nucleus segmentation in fluorescence images","volume":"95","author":"Caicedo","year":"2019","journal-title":"Cytometry Part A"},{"key":"B5","first-page":"482","article-title":"\u201cPlant response to elevated uv intensities,\u201d","volume-title":"Proceedings of the Third Conference on the Climatic Impact Assessment Program","author":"Caldwell","year":"1974"},{"key":"B6","doi-asserted-by":"publisher","first-page":"7824","DOI":"10.1016\/j.atmosenv.2006.07.031","article-title":"In situ monitoring of urban air in c\u00f3rdoba, argentina using the tradescantia-micronucleus (trad-mcn) bioassay","volume":"40","author":"Carreras","year":"2006","journal-title":"Atmos. Environ"},{"key":"B7","doi-asserted-by":"publisher","first-page":"49","DOI":"10.2307\/1310177","article-title":"Plant responses to multiple environmental factors","volume":"37","author":"Chapin","year":"1987","journal-title":"Bioscience"},{"key":"B8","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1038\/nature06603","article-title":"Preserving cell shape under environmental stress","volume":"452","author":"Cook","year":"2008","journal-title":"Nature"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1105\/tpc.9.7.1031","article-title":"Relaxation in a high-stress environment: the molecular bases of extensible cell walls and cell enlargement","volume":"9","author":"Cosgrove","year":"1997","journal-title":"Plant Cell"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.2478\/picbe-2019-0059","article-title":"\u201cBiomonitoring climate change and air quality assessment using bioindicators as experimental model,\u201d","author":"Cozea","year":"2019","journal-title":"Proceedings of the International Conference on Business Excellence, Vol. 13"},{"key":"B11","first-page":"2973","article-title":"Fundamental carcinogenic processes and their implications for low dose risk assessment","volume":"36","author":"Crump","year":"1976","journal-title":"Cancer Res"},{"key":"B12","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1590\/S1516-89132003000200017","article-title":"In situ monitoring of mutagenicity of air pollutants in s ao paulo city using tradescantia-shm bioassay","volume":"46","author":"Ferreira","year":"2003","journal-title":"Brazil. Arch. Biol. Technol"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1002\/9780470015902.a0001687.pub2","article-title":"\u201cTurgor pressure,\u201d","author":"Fricke","year":"2017","journal-title":"eLS (Encyclopedia of Life Sciences)"},{"key":"B14","first-page":"249","article-title":"\u201cUnderstanding the difficulty of training deep feedforward neural networks,\u201d","volume-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics","author":"Glorot","year":"2010"},{"key":"B15","first-page":"0","article-title":"\u201cBioindicador tradescantia clone 4430 para avaliar a polui\u00e7\u00e3o do solo por diesel,\u201d","volume-title":"Environmental Engineering Bachelor's Degree Monograph","author":"Goeldner","year":"2023"},{"key":"B16","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/0098-8472(96)01013-1","article-title":"Phytotoxicity observed in tradescantia correlates with diesel fuel contamination in soil","volume":"36","author":"Green","year":"1996","journal-title":"Environ. Exp. Bot"},{"key":"B17","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1002\/tox.20065","article-title":"Evaluation of the mutagenic potential of urban air pollution in s ao paulo, southeastern brazil, using the tradescantia stamen-hair assay","volume":"19","author":"Guimar aes","year":"2004","journal-title":"Environm. Toxicol.: An Int. J"},{"key":"B18","first-page":"770","article-title":"\u201cDeep residual learning for image recognition,\u201d","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"He","year":"2016"},{"key":"B19","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1002\/9781118567166.ch7","article-title":"\u201cEnvironmental effects on cell composition,\u201d","author":"Hu","year":"2013","journal-title":"Handbook of Microalgal Culture: Applied Phycology and Biotechnology"},{"key":"B20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/0027-5107(92)90096-K","article-title":"Tradescantia stamen-hair system as an excellent botanical tester of mutagenicity: its responses to ionizing radiations and chemical mutagens, and some synergistic effects found","volume":"270","author":"Ichikawa","year":"1992","journal-title":"Mutat. Res.\/Fundam. Mol. Mechan. Mutagen"},{"key":"B21","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/S0033-7560(69)80030-X","article-title":"Morphologically abnormal cells, somatic mutations and loss of reproductive integrity in irradiated tradescantia stamen hairs","volume":"9","author":"Ichikawa","year":"1969","journal-title":"Radiat. Botany"},{"key":"B22","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.gltp.2021.08.027","article-title":"Resnet-50 vs vgg-19 vs training from scratch: a comparative analysis of the segmentation and classification of pneumonia from chest x-ray images","volume":"2","author":"Ikechukwu","year":"2021","journal-title":"Global Trans. Proc"},{"key":"B23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/RADIOELEKTRONIKA52220.2021.9420202","article-title":"\u201cSpeaker recognition with resnet and vgg networks,\u201d","volume-title":"2021 31st International Conference Radioelektronika (RADIOELEKTRONIKA)","author":"Jakubec","year":"2021"},{"key":"B24","doi-asserted-by":"crossref","DOI":"10.1002\/9780470015902.a0022336","article-title":"\u201cBiomechanics of plant cell growth,\u201d","volume-title":"Encyclopedia of Life Sciences (ELS)","author":"Jordan","year":"2010"},{"key":"B25","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/ICCSCE58721.2023.10237088","article-title":"\u201cComparison of prostate cell image classification using cnn: Resnet-101 and vgg-19,\u201d","volume-title":"2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)","author":"Jusman","year":"2023"},{"key":"B26","doi-asserted-by":"publisher","first-page":"553","DOI":"10.3389\/fpls.2020.00553","article-title":"To lead or to follow: contribution of the plant vacuole to cell growth","volume":"11","author":"Kaiser","year":"2020","journal-title":"Front. Plant Sci"},{"key":"B27","doi-asserted-by":"publisher","first-page":"503449","DOI":"10.1016\/j.mrgentox.2022.503449","article-title":"Assessment of the mutagenic potential of the water of an urban river by means of two tradescantia-based test systems","volume":"876","author":"Khosrovyan","year":"2022","journal-title":"Mutat. Res.\/Genetic Toxicol. Environm. Mutagen"},{"key":"B28","first-page":"85","article-title":"\u201cBioindicators and biomonitors for policy, legislation and administration,\u201d","volume-title":"Trace Metals and other Contaminants in the Environment","author":"Kienzl","year":"2003"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1412.6980","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv [Preprint]."},{"key":"B30","article-title":"\u201cImagenet classification with deep convolutional neural networks,\u201d","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"B31","first-page":"57","article-title":"\u201cCarotenoids: from plants to food and feed industries,\u201d","volume-title":"Microbial Carotenoids. Methods in Molecular Biology, Vol. 1852","author":"Langi","year":"2018"},{"key":"B32","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/978-3-030-13835-6_7","article-title":"\u201cMp-idb: the malaria parasite image database for image processing and analysis,\u201d","volume-title":"Processing and Analysis of Biomedical Information: First International SIPAIM Workshop, SaMBa 2018, Held in Conjunction with MICCAI 2018","author":"Loddo","year":"2019"},{"key":"B33","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/0027-5107(94)90114-7","article-title":"Tradescantia stamen hair mutation bioassay","volume":"310","author":"Ma","year":"1994","journal-title":"Mutat. Res.\/Fundam. Mol. Mechan. Mutagen"},{"key":"B34","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1016\/S1369-5266(00)00213-2","article-title":"Shaping in plant cells","volume":"4","author":"Martin","year":"2001","journal-title":"Curr. Opin. Plant Biol"},{"key":"B35","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1105\/tpc.11.4.587","article-title":"Plant vacuoles","volume":"11","author":"Marty","year":"1999","journal-title":"Plant Cell"},{"key":"B36","unstructured":"\u201cAvalia\u00e7\u00e3o do potencial citot\u00f3xico, genot\u00f3xico e mutag\u00eanico de esgoto por meio dos sistemas-teste Allium cepa e Tradescantia pallida,\u201d106 MazivieroG. T. Rio ClaroUniversidade Estadual Paulista, Instituto de Bioci\u00eanciasBachelor's Degree Monograph2011"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.36349\/easjals.2018.v01i01.003","article-title":"Genotoxicity assessment of soil contaminated by metals\/metalloids using tradescantia pallida","author":"Meravi","year":"2018","journal-title":"East African Scholars J Agri Life Sci."},{"key":"B38","unstructured":"Survey of plant pigments: molecular and environmental determinants of plant colors716 M\u0142odzi\u0144skaE. Acta Biol. Cracov. Series Botan512009"},{"journal-title":"Biomonitoring of Air Quality Using Plants","year":"2000","author":"Mulgrew","key":"B39"},{"key":"B40","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.pbiomolbio.2007.05.001","article-title":"Fluorescence polarization in studies of bacterial cytoplasmic membrane fluidity under environmental stress","volume":"95","author":"Mykytczuk","year":"2007","journal-title":"Prog. Biophys. Mol. Biol"},{"key":"B41","unstructured":"Anthocyanin pigments: structure and biological importance4557 NassourR. AyashA. Al-TameemiK. J. Chem. Pharm. Sci132020"},{"key":"B42","unstructured":"Comparison of gene mutation frequency in tradescantia stamen hair cells detected after chernobyl and fukushima nuclear power plant accidents. Kor373378 PanekA. MiszczykJ. KimJ.-K. Cebulska-WasilewskaA. J. Environm. Biol292011"},{"key":"B43","doi-asserted-by":"publisher","first-page":"52","DOI":"10.5815\/ijisa.2021.02.04","article-title":"Implementation of transfer learning using vgg16 on fruit ripeness detection","volume":"13","author":"Pardede","year":"2021","journal-title":"Int. J. Intell. Syst. Appl"},{"key":"B44","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1016\/j.envint.2008.03.009","article-title":"Assessing the genotoxicity of urban air pollutants in varanasi city using tradescantia micronucleus (trad-mcn) bioassay","volume":"34","author":"Prajapati","year":"2008","journal-title":"Environ. Int"},{"key":"B45","doi-asserted-by":"publisher","first-page":"62830","DOI":"10.1109\/ACCESS.2020.2983774","article-title":"Sequence-dropout block for reducing overfitting problem in image classification","volume":"8","author":"Qian","year":"2020","journal-title":"IEEE Access"},{"key":"B46","doi-asserted-by":"crossref","first-page":"0945","DOI":"10.1109\/ICCSP.2019.8697909","article-title":"\u201cTransfer learning with resnet-50 for malaria cell-image classification,\u201d","volume-title":"2019 International Conference on Communication and Signal Processing (ICCSP)","author":"Reddy","year":"2019"},{"key":"B47","doi-asserted-by":"publisher","first-page":"57","DOI":"10.2478\/fsmu-2023-0005","article-title":"Air pollution monitoring with hybrid and optical sensors in curitiba and arauc\u00e1ria, brazil","volume":"78","author":"Rodrigues","year":"2023","journal-title":"Forest, Stud"},{"key":"B48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3510413","article-title":"Avoiding overfitting: a survey on regularization methods for convolutional neural networks","volume":"54","author":"Santos","year":"2022","journal-title":"ACM Comp. Surv. (CSUR)"},{"key":"B49","doi-asserted-by":"publisher","first-page":"101214","DOI":"10.1016\/j.jestch.2022.101214","article-title":"Towards automated eye cancer classification via vgg and resnet networks using transfer learning","volume":"35","author":"Santos-Bustos","year":"2022","journal-title":"Eng. Sci. Technol. Int. J"},{"key":"B50","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1289\/ehp.782751","article-title":"Exploratory monitoring of air pollutants for mutagenicity activity with the tradescantia stamen hair system","volume":"27","author":"Schairer","year":"1978","journal-title":"Environ. Health Perspect"},{"key":"B51","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/978-1-4613-3455-2_11","volume-title":"Genotoxic Effects of Airborne Agents. Environmental Science Research, Vol. 25","author":"Schairer","year":"1982"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1409.1556","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv [Preprint]."},{"volume-title":"Radiobiologic Studies of Tradescantia Plants Orbited in Biosatellite 2","year":"1971","author":"Sparrow","key":"B53"},{"key":"B54","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/S0027-5107(74)80024-2","article-title":"Comparison of somatic mutation rates induced in tradescantia by chemical and physical mutagens","volume":"26","author":"Sparrow","year":"1974","journal-title":"Mutat. Res.\/Fund. Mol. Mechan. Mutagen"},{"key":"B55","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/S0091-679X(08)62139-1","article-title":"Evaluation of turgidity, plasmolysis, and deplasmolysis of plant cells","volume":"2","author":"Stadelmann","year":"1966","journal-title":"Meth. Cell Biol"},{"key":"B56","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1104\/pp.59.2.285","article-title":"Effect of turgor pressure and cell size on the wall elasticity of plant cells","volume":"59","author":"Steudle","year":"1977","journal-title":"Plant Physiol"},{"key":"B57","first-page":"1139","article-title":"\u201cOn the importance of initialization and momentum in deep learning,\u201d","volume-title":"International Conference on Machine Learning","author":"Sutskever","year":"2013"},{"key":"B58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-0114-9","article-title":"Detecting affect states using vgg16, resnet50 and se-resnet50 networks","volume":"1","author":"Theckedath","year":"2020","journal-title":"SN Comp. Sci"},{"key":"B59","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4615-8972-3_7","article-title":"Tradescantia Stamen Hairs: a Radiobiological Test System Applicable to Chemical Mutagenesis","author":"Underbrink","year":"1973"},{"key":"B60","first-page":"1","article-title":"\u201cUncertainty-aware contour proposal networks for cell segmentation in multi-modality high-resolution microscopy images,\u201d","volume-title":"Competitions in Neural Information Processing Systems","author":"Upschulte","year":"2023"},{"key":"B61","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1046\/j.1365-3040.2002.00910.x","article-title":"Profiles of light absorption and chlorophyll within spinach leaves from chlorophyll fluorescence","volume":"25","author":"Vogelmann","year":"2002","journal-title":"Plant, Cell"},{"key":"B62","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1126\/science.150.3702.1413","article-title":"Transpiration and the stomata of leaves: Water loss through leaf pores is controlled by pore size, which varies with environment and chemical sprays","volume":"150","author":"Waggoner","year":"1965","journal-title":"Science"},{"key":"B63","first-page":"82","article-title":"\u201cDeep convolutional neural networks for detecting cellular changes due to malignancy,\u201d","volume-title":"Proceedings of the IEEE International Conference on Computer Vision Workshops","author":"Wieslander","year":"2017"},{"volume-title":"Dive into Deep Learning","year":"2023","author":"Zhang","key":"B64"},{"key":"B65","doi-asserted-by":"publisher","first-page":"476","DOI":"10.3389\/fpls.2014.00476","article-title":"Plant vacuole morphology and vacuolar trafficking","volume":"5","author":"Zhang","year":"2014","journal-title":"Front. Plant Sci"}],"container-title":["Frontiers in Big Data"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2024.1384240\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T01:10:15Z","timestamp":1731978615000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdata.2024.1384240\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,15]]},"references-count":65,"alternative-id":["10.3389\/fdata.2024.1384240"],"URL":"https:\/\/doi.org\/10.3389\/fdata.2024.1384240","relation":{},"ISSN":["2624-909X"],"issn-type":[{"type":"electronic","value":"2624-909X"}],"subject":[],"published":{"date-parts":[[2024,5,15]]},"article-number":"1384240"}}