PDF(Pubmed)
Abstract：
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.
摘要：
紫草属植物是一个复杂的系统，是敏感的环境因素，如供水，pH值,温度,光,辐射,杂质,和营养可用性。它可以用作环境变化的生物监测器；但是，生物测定是耗时的，并且具有很强的人为干扰因素，可能会根据进行分析的人而改变结果。我们已经开发了计算机视觉模型来研究Tradescantia克隆4430植物雄蕊毛细胞的颜色变化，由于空气污染和土壤污染，可以强调。这项研究引入了一个新的数据集，Trad-204,其包含来自Tradescantia克隆4430的单细胞图像,在Tradescantia雄毛突变生物测定(Trad-SHM)期间捕获。数据集包含来自两个实验的图像，一个侧重于颗粒物对空气的污染，另一个侧重于被柴油污染的土壤。两个实验都是在库里蒂巴进行的,巴西,2020年至2023年。图像代表不同形状的单细胞，尺寸,和颜色,反映植物对环境压力的反应。开发了一种自动分类任务来区分蓝色和粉红色细胞，这项研究探索了一个基线模型和三个人工神经网络(ANN)架构，即,TinyVGG,VGG-16和ResNet34。紫丁香对土壤中的空气颗粒物浓度和柴油都具有敏感性。结果表明，残差网络体系结构在训练集和测试集上的准确性均优于其他模型。数据集和发现有助于理解植物细胞对环境胁迫的反应，并为植物细胞自动图像分析的进一步研究提供宝贵的资源。讨论强调了膨大压力对细胞形状的影响以及对植物生理学的潜在影响。神经网络架构之间的比较与以前的研究一致，强调ResNet模型在图像分类任务中的卓越性能。粉红色细胞的人工智能识别提高了计数准确性，从而避免了由于不同颜色感知而造成的人为错误，疲劳,或者注意力不集中,除了促进和加快分析过程。总的来说,该研究提供了对植物细胞动力学的见解，并为未来研究如细胞形态变化提供了基础。这项研究证实，生物监测应被视为政治行动的重要工具，是风险评估和制定与环境有关的新公共政策的相关问题。
