1Purpose:

The types and quantities of phytoplankton in water bodies, as well as their particle size distribution, are important criteria for studying the water environment. However, manual judgment has always been used, which is quite time-consuming and labor-intensive. Wan Shen AlgaeAC Enhanced Type（The AlgaeAC-P algae automatic classification and counting instrument can effectively solve the pain point problem of users. It is mainly used in ecological investigation, fisheries, aquaculture, education and other industries to automatically classify and count phytoplankton (algae) samples in water, measure their size, classify their species, and determine their biomass. The AlgaeAC-P model also comes with an intelligent identification module for algae and planktonic animals, which helps reduce the heavy identification workload in the past and is an essential tool for ecological investigation and monitoring.

IIPerformancetechnologyParameter indicators:

▲ 1. Full time autofocus with high resolution and large field of view optical imaging of 24 million pixels or more, can automatically stitch 625 automatic camera fields into nearly 5 billion pixel super field of view images, effectively avoiding algae from being automatically shredded by the edges of each field of view. Imaging supports a full range of objective lenses such as 10X, 20X, and 40X. The system is equipped with an automatic classification and recognition library for 20X objective imaging. A full-time autofocus algorithm optimized for microalgae to ensure clear scanning images. The system contains a classification and recognition library for 97 common genera and species of algae in the phyla Cyanobacteria, Diatom, Chlorophyta, Bacteroidetes, Cryptophyta, Chrysophyta, and Cyanobacteria. It can be expanded to 120 genera and species based on local conditions and supports online updates of the recognition library.

2. After pre-treatment, the water sample is placed in the algae counting box, and the entire process of algae recognition and classification counting is automatically completed with one click (automatic moving field of view focusing, scanning, photography, automatic classification, recognition, counting, and automatic generation of statistical reports). The detection is based on the "SL733-2016 Technical Regulations for Monitoring phytoplankton in Inland Waters", "Methods for Monitoring and Analyzing Water and Wastewater" (Fourth Edition), Part 5 "Biological Monitoring Methods for Water and Wastewater", as well as the algae monitoring standards in GB17378-2007 "Marine Monitoring Specification", GB/T12763-2007 "Marine Survey Specification", and HJ 1216-2021 "Determination of Water Quality Phytoplankton 0.1mL Counting Box Microscopic Counting Method".

3. It can analyze and obtain morphological parameters such as area, perimeter, volume, length, width, main axis, secondary axis, and equivalent diameter of each algal body. Can analyze and count the quantity, area, volume, and proportion of various algae (by phylum or genus); Sort and display the proportion of each category in a bar chart. Further statistical analysis of data can be conducted in Excel software. Algae names can be directly marked on the collected images, and images of each algae can be extracted and segmented, automatically classified and saved. Historical data can be viewed retrospectively. Automatically provide a classification and counting statistical report, indicating dominant species and dominance, and sorting by dominant species. Automatically calculate Shannon Wiener index, evenness index, richness index, algal individual density, algal cell density, biomass, etc.

4. Can automatically classify and analyze algae ranging from 3 to 1000 μ m, with automatic scanning imaging and analysis time of about 20 minutes for 100 fields of view (optional for 25-400 fields of view); The detection range is 10 ^ 5-10 ^ 10 pieces/liter; The automatic recognition rate of dominant species in the local classification recognition library is ≥ 90%, the comprehensive automatic recognition rate is ≥ 80%, and the final recognition rate after interactive correction can reach over 98%; At a concentration of 10 ^ 7-10 ^ 8 per liter, the repeatability error of automatic analysis is less than 5%.

▲ 5. Imitating the process of detecting algae with an artificial microscope, imaging counting can be performed using five counting methods: whole slide counting, diagonal counting, grid counting, and random field counting. Chinese and Latin bilingual display of plankton expert database: 15 phyla, 1718 genera, and 15793 species of algae; There are 26 major categories, 2002 genera, and 9843 species of planktonic animals. Covering common algae and planktonic animals in various river basins and sea areas in China. Before signing the contract, it is necessary to provide on-site prototype testing confirmation. There are currently over 291109 valid image libraries, and each library's genus and content can be expanded independently. It is also possible to search and identify copepods by P5 chest foot. It can automatically index algae and planktonic animals in the user's established count table to generate a small database of the watershed of interest, making it faster and more accurate to search for images and identify resources. It can be located and annotated on the map based on the geographic coordinates of the collection location, supporting various map sources such as Amap, Amap, Google Maps, and Google Satellite Maps.

6. The manufacturer provides assistance in establishing a local classification initial identification database service, remote assistance guidance, and 5-year free remote upgrade service.

7. The Microcystis analysis module can automatically learn and analyze the cell count of clustered Microcystis populations, and automatically count planktonic animals such as granular or single-cell microalgae, chain microalgae cells, nematodes, etc. Capable of counting and measuring the morphology of algae and planktonic animals, and compiling and reporting the sequences of dominant species.

8. Built in 34 geometric models, the volume and biomass of planktonic organisms can be calculated by measuring a small number of parameters.

3、 Configuration List:

1. One set of Wanshen algae automatic classification and counting software (including plankton intelligent identification system)

2. High precision electronic control X-Y automatic scanning platform+1 set of controller

3. One set of high-resolution and wide field optical imaging system with full-time autofocus

4. 1 branded computer (Core i5 11th generation or above CPU/32GB memory/6G or above GPU card with CUDA support/256GB solid-state drive+1T hard drive/23 "color display, 1 USB 3.0 port+3 USB 2.0 ports, running on Windows 10 operating system); At the same time, an additional Olympus BX53 three eye biological microscope is required for use.

Note: This technical proposal includes▲The payment must be responsive, otherwise it is a significant deviation.