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Algal blooms have seriously affected the production and life of people and real-time detection of algae in water samples is a powerful measure to prevent algal blooms. The traditional manual detection of algae with a microscope is extremely time-consuming. In recent years, although there have been many studies using deep learning to classify and detect algae, most of them have focused on the relatively simple task of algal classification. In addition, some existing algal detection studies not only use small datasets containing limited algal species, but also only prove that object detection algorithms can be applied to algal detection tasks. These studies cannot implement the real-time detection of algae and timely warning of algal blooms. Therefore, this paper proposes an efficient self-organized detection system for algae. Benefiting from this system, we propose an interactive method to generate the algal detection dataset containing 28,329 images, 562,512 bounding boxes and 54 genera. Then, based on this dataset, we not only explore and compare the performance of 10 different versions of state-of-the-art object detection algorithms for algal detection, but also tune the detection system we built to its optimum state. In practical application, the system not only has good algal detection results, but also can complete the scanning, photographing and detection of a 2 cm × 2 cm, 0.1 mL algal slide specimen within five minutes (the resolution is 0.25886 μ m/pixel); such a task requires a well-trained algal expert to work continuously for more than three hours. The efficient algal self-organized detection system we built makes it possible to detect algae in real time. In the future, with the help of IoT, we can use various smart sensors, actuators and intelligent controllers to achieve real-time collection and wireless transmission of algal data, use the efficient algal self-organized detection system we built to implement real-time algal detection and upload the detection results to the cloud to realize timely warning of algal blooms. [ABSTRACT FROM AUTHOR]

• Cyanobacteria impacted plankton bio-diversity and resource use efficiency. • No clear relation between cyanobacteria and phytoplankton absolute abundances. • Cyanobacteria had opposite relation with phytoplankton and zooplankton bio-diversities. • Cyanobacteria increased total algae and cladocerans RUEs. • Cyanobacteria decreased phytoplankton and rotifers RUEs. Cyanobacterial blooms are global threats to freshwater ecosystem functioning, human health, and ecoservices. We assessed impacts of cyanobacterial bloom intensity on plankton ecosystem functioning using eukaryotic phytoplankton and zooplankton indicators and associated key physicochemical data collected from four seasons of two years at 24 evenly distributed sites in Lake Taihu that has year-around cyanobacterial blooms. Our analyses involved comparison of four site-groups with different bloom intensities and analyzing all sampling sites together using comparison, hierarchical partitioning analysis, generalized additive mixed model, and structural equation model. We found that cyanobacterial abundance positively associated with TP and temperature (negatively with TN:TP), while phytoplankton positively associated with TN. There was an inverse relation trend between relative abundances of phytoplankton and cyanobacteria, but there was no clear trend between absolute abundances of phytoplankton and cyanobacteria. Rotifers were most dominant when cyanobacteria were unabundant, while cladocerans presented higher abundance when cyanobacteria were in high abundance. Phytoplankton functional richness and species richness negatively and zooplankton functional richness and species richness positively associated with cyanobacterial bloom intensity. Cyanobacterial bloom intensity negatively associated with resource use efficiencies (RUEs) of phytoplankton and rotifers, and positively associated with RUE of cladocerans. Our analytical approach of integrating comparison of site-groups and analyzing all sites together uncovered how cyanobacterial bloom intensity shifted and altered physicochemical and biological conditions and plankton ecosystem functioning, and identified the mechanism and strength of the interactive linkages among physicochemical and biological indicators. Although our results may be different from oligotrophic lakes or reservoirs, our findings provide new insights in understanding the impacts of cyanobacterial bloom intensity on the dynamics of plankton communities and ecosystem functioning for polymictic eutrophic lakes, which may have broad application in enhancing the knowledge of this subject and provides the science base for managing polymictic eutrophic lake water quality and ecosystem functioning. [ABSTRACT FROM AUTHOR]