This product is developed in accordance with Water quality — Determination of phytoplankton — 0.1 ml chamber — Microscope counting method (HJ 1216—2021). Based on deep learning technology and knowledge base, algae AI recognition model is built. Through automatic sampling technology and fully automatic microscopic photography technology, detailed information such as algae genus, algae density, dominance, diversity, etc. in the water body can be obtained, realizing automatic and intelligent analysis of water algae genus.
Full process automation. The system has automatic sample injection, microscopic photography, and fully automated analysis and counting. After a sample is injected, no professional is required. The sample’s algae genus, algae density, biomass, and diversity information are automatically output, avoiding human errors.
Fast analysis. The analysis of a sample only takes 30 minutes. Compared with 3 hours it takes by professionals, the automatic analysis greatly improves detection efficiency.
High recognition accuracy. Based on the relevant certified standard algae atlas database, using deep neural network technology, it can identify more than 100 algae genera in multiple basins, and its recognition accuracy exceeds similar products.
Result verification. Users can verify the recognition results by an algae standard morphology library, thus correcting incorrect recognition results to ensure the accuracy of the test.
The instrument can be used for early warning of algal blooms. Combined with monitoring platforms such as buoys, sailing ships, unmanned ships, and automatic water quality monitoring stations, the instrument helps to conduct emergency, rapid, and large-scale source tracing and scope investigation of ecological disasters such as algal blooms.
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