Abstract:

Plankton are essential components of aquatic ecosystems, and variations in their community structure, abundance, and dominant species succession can reflect changes in water trophic status, ecological disturbances, and environmental quality. Therefore, plankton serve as important bioindicators in aquatic ecological monitoring, harmful algal bloom early warning, and water quality assessment. Traditional microscopic identification methods rely heavily on expert experience and are characterized by low efficiency, strong subjectivity, and limited capability for continuous monitoring, making it difficult to meet the it difficult real-time, fine-scale, and automated aquatic environmental management. As imaging technologies and image recognition algorithms advance, plankton monitoring has gradually shifted from manual sampling and microscopic identification toward automated imaging and intelligent recognition. Based on this development trend, this review systematically summarized the current progress of plankton imaging devices, publicly available datasets, and image recognition technologies from the perspectives of image acquisition, data support, methodological evolution, and application scenarios. The transition of recognition approaches from handcrafted feature extraction and traditional machine learning to deep learning-based methods was analyzed, along with recent applications in community monitoring and ecological response analysis, harmful algal bloom prediction, and water quality assessment. Overall, microscopic imaging, flow imaging, and in situ imaging technologies were shown to provide diverse approaches for plankton image acquisition, with in situ imaging being better suited to continuous monitoring in natural aquatic environments. Public datasets established through expert annotation or manual verification were found to provide reliable data foundations for model training and performance evaluation. Deep learning has become a major technical approach to plankton image recognition, demonstrating significant advantages in improving recognition accuracy and automation capability. However, challenges remain, including insufficient dataset standardization, limited model generalization ability, high field-deployment costs, and inadequate integration of multi-source information. In the future, emerging technologies such as self-supervised learning, Vision Transformer (ViT), and edge intelligence are expected to reduce dependence on manual annotation, enhance feature representation for complex images, and improve field deployment capabilities. Integrating in situ imaging monitoring, automated recognition methods, and water quality parameter analysis will further enhance the application potential of plankton image recognition technologies in aquatic environmental monitoring, early warning, and assessment.

Key words: plankton, image recognition, machine learning, deep learning, water environment management