AlgaeC Phytoplankton Intelligent Identification and Counting System

I. Basis and Standards for Analysis

Complies with the specifications for algae monitoring as stipulated in "SL733-2016 Technical Regulations for Monitoring Phytoplankton in Inland Waters," Part V "Biological Monitoring Methods for Water and Wastewater" of "Water and Wastewater Monitoring and Analysis Methods" (Fourth Edition, 2002), GB17378-2007 "Specifications for Marine Monitoring," GB/T12763-2007 "Specifications for Oceanographic Survey," and the requirements of HJ 1216-2021 "Water Quality—Determination of Phytoplankton—Microscopic Counting Method Using 0.1 mL Counting Chamber."

II. Main Performance Indicators

1) Microscopic Imaging: Allows manual control for observing, capturing, storing microscopic images, and automatically capturing up to 200 images at equal intervals. Features real-time preview saturation warning and automatic background correction.

2) ★ Expert image database for zooplankton with bilingual display in Chinese and Latin (26 major categories, 2,002 genera, 9,846 species). The existing valid image database contains over 89,656 images, and the genera/species and content of each database can be expanded.

3) ★ Intelligent image-based search feature that effectively compares zooplankton images based on similarity in shape, texture, etc., enabling rapid identification of zooplankton. One-click image search displays similar species ranked from highest to lowest similarity. Supports image-based search for identifying copepods using P5 thoracic legs.

4) ★ Zooplankton counting and morphological measurement functions: a. Plankton classification marking: Uses different colors and sizes of colored circles to mark various plankton, and automatically accumulates counts by category for up to 200 captured images (can merge counting results from different magnifications and multiple samples). b. Automatic ranking of dominant species, sorting by phylum/class, and analysis of dominant community composition percentages. c. Automatic calculation of Shannon-Wiener index, evenness index, automatic conversion of algal density, and automatic conversion of zooplankton abundance. d. Assists in calculating plankton biomass using numerous shape models (includes 34 geometric models; individual/cell volume can be calculated by measuring a few parameters).

5) ★ Superior multi-focus fusion 3D high-definition imaging with extended depth of field. Automatic stitching, cropping, editing, and correction of multi-field images. Features automatic learning and classification of zooplankton color and shape, allowing monitoring and correction of zooplankton categories.

6) ★ Automatic image matting feature for plankton cells, enabling rapid extraction of main edge feature images. Includes clarity enhancement processing for blurred and overlapping plankton images.

7) Analysis software supports online self-upgrading.

III. Configuration:

1) Professional-grade 20-megapixel color CMOS camera (Sony 1-inch large chip), standard C-mount for trinocular microscope

2) Plankton counting analysis intelligent identification system software (zooplankton identification module) – 1 set

3) Brand computer (12th Gen or higher Core i5; CPU/16GB RAM/21" color display/wireless network card, 1 USB 3.0 port + 3 USB 2.0 ports, running Windows 10 or 11 full professional edition) – 1 unit

Note: Items marked with ★ in this technical proposal must be addressed; otherwise, it will be considered a major deviation.

The trinocular microscope needs to be purchased separately. Recommendation: Olympus BX53 or higher-grade research-level: Olympus BX53T-32P01 research-grade trinocular biological microscope (includes BX53F frame, trinocular observation tube, D-type objective turret, BX3 arm, 10x wide-field adjustable eyepiece), Sunny flat-field semi-apochromatic objectives (20X, 10X).