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Multi-modal learning-based algae phyla identification using image and particle modalities
Water Research ( IF 11.4 ) Pub Date : 2025-01-21 , DOI: 10.1016/j.watres.2025.123172
Do Hyuck Kwon, Min Jun Lee, Heewon Jeong, Sanghun Park, Kyung Hwa Cho
Water Research ( IF 11.4 ) Pub Date : 2025-01-21 , DOI: 10.1016/j.watres.2025.123172
Do Hyuck Kwon, Min Jun Lee, Heewon Jeong, Sanghun Park, Kyung Hwa Cho
Algal blooms in freshwater, which are exacerbated by urbanization and climate change, pose significant challenges in the water treatment process. These blooms affect water quality and treatment efficiency. Effective identification of algal proliferation based on the dominant species is important to ensure safe drinking water and a clean water supply. Traditional algae identification techniques, such as microscopy and molecular techniques, are time-consuming and depend on the expertise of the practitioner. This study introduced an artificial intelligence (AI)-based multi-modal approach, which is a recent advancement in techniques for improving algal identification by integrating algal images and particle properties. We employed multi-modal learning to integrate multiple data modalities, including algal images and particle properties acquired using Flow Cam, to provide robustness and reliability for classifying algal phyla, such as Anabaena, Aphanizomenon, Microcystis, Oscillatoria, and Synedra. This study involved acquiring images and particle modalities, which were conducted to integrate the dataset using early, late, and hybrid fusion methods. In addition, explainable AI approaches, including SHapley Additive exPlanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM), were used to investigate the contributions of the algal image and particle modalities to the proposed multi-modal algorithm. The multi-modal algae identifier with late fusion achieved an average F1 score of 0.91 and 0.88 for training and tests related to identifying algal phyla, respectively. Furthermore, compared with other modalities, the image and particle modalities showed significant potential as complementary and reliable components of deep-learning algorithms for algal identification in the water treatment process. These findings can contribute to a safe and clean water supply by effectively identifying the dominant algal species in the water treatment process.
中文翻译:
基于多模态学习的基于图像和粒子模态的藻类门鉴定
城市化和气候变化加剧了淡水中的藻华,对水处理过程构成了重大挑战。这些水华会影响水质和处理效率。根据优势物种有效识别藻类增殖对于确保安全饮用水和清洁水供应非常重要。传统的藻类鉴定技术,如显微镜和分子技术,非常耗时,并且取决于从业者的专业知识。本研究引入了一种基于人工智能 (AI) 的多模态方法,这是通过整合藻类图像和颗粒特性来改进藻类识别的技术的最新进展。我们采用多模态学习来集成多种数据模态,包括使用 Flow Cam 获取的藻类图像和颗粒特性,为藻类门的分类提供稳健性和可靠性,例如 Anabaena、Aphanizomenon、Microcystis、Oscillatoria 和 Synedra。这项研究涉及获取图像和粒子模态,这些图像和粒子模态是为了使用早期、晚期和混合融合方法整合数据集。此外,可解释的 AI 方法,包括 SHapley 加法解释 (SHAP) 和梯度加权类激活映射 (Grad-CAM),用于研究藻类图像和粒子模态对所提出的多模态算法的贡献。具有晚期融合的多模态藻类标识符在与识别藻门相关的训练和测试中分别取得了 0.91 和 0.88 的平均 F1 分数。 此外,与其他模式相比,图像和粒子模式显示出作为水处理过程中藻类识别深度学习算法的补充和可靠组成部分的巨大潜力。这些发现可以通过有效识别水处理过程中的主要藻类物种来促进安全和清洁的供水。
更新日期:2025-01-21
中文翻译:
基于多模态学习的基于图像和粒子模态的藻类门鉴定
城市化和气候变化加剧了淡水中的藻华,对水处理过程构成了重大挑战。这些水华会影响水质和处理效率。根据优势物种有效识别藻类增殖对于确保安全饮用水和清洁水供应非常重要。传统的藻类鉴定技术,如显微镜和分子技术,非常耗时,并且取决于从业者的专业知识。本研究引入了一种基于人工智能 (AI) 的多模态方法,这是通过整合藻类图像和颗粒特性来改进藻类识别的技术的最新进展。我们采用多模态学习来集成多种数据模态,包括使用 Flow Cam 获取的藻类图像和颗粒特性,为藻类门的分类提供稳健性和可靠性,例如 Anabaena、Aphanizomenon、Microcystis、Oscillatoria 和 Synedra。这项研究涉及获取图像和粒子模态,这些图像和粒子模态是为了使用早期、晚期和混合融合方法整合数据集。此外,可解释的 AI 方法,包括 SHapley 加法解释 (SHAP) 和梯度加权类激活映射 (Grad-CAM),用于研究藻类图像和粒子模态对所提出的多模态算法的贡献。具有晚期融合的多模态藻类标识符在与识别藻门相关的训练和测试中分别取得了 0.91 和 0.88 的平均 F1 分数。 此外,与其他模式相比,图像和粒子模式显示出作为水处理过程中藻类识别深度学习算法的补充和可靠组成部分的巨大潜力。这些发现可以通过有效识别水处理过程中的主要藻类物种来促进安全和清洁的供水。