Tradescantia response to air and soil pollution, stamen hair cells dataset and ANN color classification.

Autor:	Rodrigues LT; Graduate Program of Environmental Engineering, Federal University of Paraná, Curitiba, Brazil., Goeldner BSA; Department of Environmental Engineering, Federal University of Paraná, Curitiba, Brazil., Mercuri EGF; Department of Environmental Engineering, Federal University of Paraná, Curitiba, Brazil., Noe SM; Institute of Forestry and Engineering, Estonian University of Life Sciences, Tartu, Estonia.
Jazyk:	angličtina
Zdroj:	Frontiers in big data [Front Big Data] 2024 May 15; Vol. 7, pp. 1384240. Date of Electronic Publication: 2024 May 15 (Print Publication: 2024).
DOI:	10.3389/fdata.2024.1384240
Abstrakt:	Tradescantia 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 Tradescantia 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 Tradescantia clone 4430, captured during the Tradescantia 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. Tradescantia 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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Rodrigues, Goeldner, Mercuri and Noe.)
Databáze:	MEDLINE
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