Abstract：
Monitoring chlorophyll-a concentrations (Chl-a, μg·L-1) in aquatic ecosystems has attracted much attention due to its direct link to harmful algal blooms. However, there has been a lack of a cost-effective method for measuring Chl-a in small waterbodies. Inspired by the increase of smartphone photography, a Smartphone-based convolutional neural networks (CNN) framework (SCCA) was developed to estimate Chl-a in Aquatic ecosystem. To evaluate the performance of SCCA, 238 paired records (a smartphone image with a 12-color background and a measured Chl-a value) were collected from diverse aquatic ecosystems (e.g., rivers, lakes and ponds) across China in 2023. Our performance-evaluation results revealed a NS and R2 value of 0.90 and 0.94 in Chl-a estimation, demonstrating a satisfactory (NS = 0.84, R2 = 0.86) model fit in lower Chl-a (<30 μg L-1) conditions. SCCA had involved a realtime-update method with hyperparameter optimization technology. In comparison with the existing methods of measuring Chl-a, SCCA provides a useful screening tool for cost-effective measurement of Chl-a and has the potential for being an algal bloom screening means in small waterbodies, using Huajin River as a case study, especially under limited resources for water measurement. Overall, we highlight that the SCCA can be potentially integrated into a smartphone application in the future to diverse waterbodies in environmental management.
摘要：
监测叶绿素a浓度(Chl-a，水生生态系统中的μg·L-1）由于与有害藻华直接相关而备受关注。然而,一直缺乏一种经济有效的方法来测量小水体中的Chl-a。灵感来自智能手机摄影的增加，开发了基于智能手机的卷积神经网络（CNN）框架（SCCA）来估计水生生态系统中的Chl-a。为了评估SCCA的性能，从不同的水生生态系统中收集了238条配对记录（带有12色背景和测得的Chl-a值的智能手机图像）（例如，河流,湖泊和池塘)在2023年在中国各地。我们的性能评估结果显示，Chl-a估计的NS和R2值为0.90和0.94，在较低的Chl-a（<30μgL-1）条件下，证明了令人满意的（NS=0.84，R2=0.86）模型拟合。SCCA采用了超参数优化技术的实时更新方法。与现有的Chl-a测量方法相比，SCCA为Chl-a的经济有效测量提供了有用的筛选工具，并且有可能成为小水体中的藻类水华筛选手段，以华锦河为例，特别是在水资源测量有限的情况下。总的来说,我们强调，SCCA将来可能会集成到智能手机应用程序中，以适应环境管理中的各种水体。
