Yuan A, Wang B, Li J, Lee Joseph H W
School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao Special Administrative Region of China.
School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Taipa, Macao Special Administrative Region of China.
Water Res. 2023 Apr 15;233:119727. doi: 10.1016/j.watres.2023.119727. Epub 2023 Feb 10.
Harmful Algal Blooms (HAB) are damaging to ecosystem functions and pose challenges to environmental and fisheries management. The key to HAB management and understanding the complex algal growth dynamics is the development of robust systems for real-time monitoring of algae populations and species. Previous algae classification studies mainly rely on the combination of an in-situ imaging flow cytometer and an off-site lab-based algae classification model such as Random Forest (RF) for the analysis of high-throughput images. An on-site AI algae monitoring system on top of an edge AI chip embedded with the proposed Algal Morphology Deep Neural Network (AMDNN) model is developed to achieve real-time algae species classification and HAB prediction. Based on a detailed examination of real-world algae images, dataset augmentation is first performed: consisting of orientation, flipping, blurring, and Resizing with Aspect ratio Preserved (RAP). The dataset augmentation is shown to significantly improve classification performance which is superior to that of the competitive RF model. And the attention heatmaps show that for relatively regular-shaped algal species (e.g., Vicicitus), the model weights the color and texture information heavily; while the shape-related features are more important for complex-shaped algae (e.g., Chaetoceros). The AMDNN is tested on a dataset of 11,250 algae images containing the 25 most common HAB classes in Hong Kong subtropical waters with 99.87% test accuracy. Based on the fast and accurate algae classification, the AI-chip-based on-site system is applied to a one-month dataset in February 2020; the predicted trends of total cell counts and targeted HAB species counts are in good agreement with observations. The proposed edge AI algae monitoring system provides a platform for the development of practical HAB early warning systems that can effectively support environmental risk and fisheries management.
有害藻华（HAB）对生态系统功能具有破坏作用，并给环境和渔业管理带来挑战。有害藻华管理以及理解复杂藻类生长动态的关键在于开发强大的系统，用于实时监测藻类种群和物种。先前的藻类分类研究主要依靠原位成像流式细胞仪与基于异地实验室的藻类分类模型（如随机森林（RF））相结合，来分析高通量图像。基于嵌入所提出的藻类形态深度神经网络（AMDNN）模型的边缘人工智能芯片，开发了一种现场人工智能藻类监测系统，以实现藻类物种的实时分类和有害藻华预测。基于对实际藻类图像的详细检查，首先进行数据集增强：包括旋转、翻转、模糊以及保持宽高比的缩放（RAP）。结果表明，数据集增强显著提高了分类性能，优于具有竞争力的RF模型。注意力热图显示，对于形状相对规则的藻类物种（如颤藻属），模型对颜色和纹理信息的权重较大；而对于形状复杂的藻类（如角毛藻属），与形状相关的特征更为重要。AMDNN在包含香港亚热带水域25种最常见有害藻华类别的11250张藻类图像数据集上进行测试，测试准确率达到99.87%。基于快速准确的藻类分类，基于人工智能芯片的现场系统应用于2020年2月的一个月数据集；总细胞计数和目标有害藻华物种计数的预测趋势与观测结果高度吻合。所提出的边缘人工智能藻类监测系统为开发实用的有害藻华早期预警系统提供了一个平台，能够有效支持环境风险和渔业管理。
