AlgaeAI 500 Intelligent Monitoring System for Planktonic Algae

Quick numbersAlgaeAI 500Intelligent monitoring system for planktonic algae, equipped with Olympus scientific grade biological microscopeBX53Industrial digital camerasandAlgaeAIComposition of artificial intelligence systems. CanHigh quality microscopic observation, photography, and intelligent recognitionPhytoplankton, classification and counting, biomass measurement, calculation of algal cell density, automatic generation and export of data reports to achieve electronic recording of phytoplankton and ensure the integrity of electronic data.

Artificial intelligence, within reach

AlgaeAI 500Algae using machine learningAIUsing models to carry out classification, identification, and counting analysis of phytoplankton, No deep expertise in algal biology is required.

l Simple, one click completion of identification, classification, counting, percentage, algae density calculation, dominant algae sorting, and report output

l Fast, single field classification and counting of algae cells in less than 3 seconds

l Accurate and recognizable, trained by neural networks85Common algae with high recognition accuracy90%above

l Adapt to common complex microscopic images in experiments, such as overlapping algal cells and incomplete algal cell morphology

One click operation, transparent throughout the entire process

The analysis, recognition, and statistical process are fully displayed on the screen, and the operator can clearly observe the analysis process and processing results of each image.

l click“AIstart”The main window images flashed one by one, and the algae cells were framed one by one, with the name of the algae on the top of the frame

l The green scrollbar on the upper right indicates that the sample is currently being tested……

l On the right is real-time jumping updated data: phylum, algae name, algae quantity, percentage, algae density……

Detection completed, click on the image queue in the bottom left corner to easily view the algae count on each image: individual names are incorrect? Individual algae not detected? Do individual algae need to be removed? Simple, click on the toolbox,2-3It can be corrected in seconds.

AIEasily solve the classification and counting of algae cells

AlgaeAI 500After training, debugging, and optimization with big data models, it is possible to automatically classify and count algal cells with high accuracy. Algae cells are evenly distributed, but when the concentration is high (appearing in a single field of view)30-150Cell overlap and adhesion often occur,AlgaeAI 500Can handle it easily. Just need to shoot20-50A single field of view can calculate the concentration of cells per liter without the need for expensive electric platforms, especially during the occurrence of algal blooms, without dilution, allowing for quick and intelligent counting.

algaeClass selfDynamic classification counting: Algae cells are evenly distributed, with varying depths of structural contours

Automatic classification and counting of algae: spindle shaped cells overlap with each other, blurred contours, and some only display local body types

Automatic classification and counting of algae: long rod-shaped cells criss crossing

Research grade microscope, beautifully interpreting image quality

High quality microscopic imaging is the foundation for observing the morphological details of phytoplankton and improving recognition and counting accuracy.AlgaeAI 500It is from OlympusBX53A digital imaging system consisting of a biological microscope and a high-sensitivity global shutter camera.BX53Adopting the latestUIS2Infinite optical system, high-quality mirror coating eliminates stray light on the inner surface, thereby achieving good sensitivity and color separationWide, bright, and uniform field of view.

The system has been configured4XThe10XThe20XThe40XThe100XObjective lens, suitable for observing plankton of different sizes. The most commonly used40The magnifying lens adopts a flat field semi complex achromatic objective lens, combined with a large target area global shutter camera, to capture images during rapid movement of glass slides. The imaging is still sharp and free of tailing, which effectively displays the color, morphology, and pattern structure of algae cells, providing high-quality images for accurate recognition.

Main functions and technical indicators

1. Analysis standards

Compliant with《SL733-2016Technical Regulations for Monitoring Phytoplankton in Inland Waters and Technical Requirements for Water Ecological Monitoring-Freshwater phytoplankton "《HJ1216-2021Determination of phytoplankton in water quality0.1mLCounting box-Microscope Counting Method "and《HJ1215-2021Water quality - Determination of phytoplankton - Filter membrane-Microscopic Counting Method "," Monitoring and Analysis Methods for Water and Wastewater "(Fourth Edition), and《GB17378-2007The algae analysis requirements corresponding to the Marine Monitoring Standards.

2. OlympusBX53digitalMicroscopic imaging system

Ø Optical system:UIS2Infinite optical system

Ø Illuminator: Built in transmitted light Kohler Illuminator, light intensity preset button, light intensityLEDmonitor

Ø Focus: Vertical moving stage, travel25mm,Equipped with coarse adjustment limiter,The coarse adjustment knob can adjust the torque. The installation position of the loading platform can be changed,Fine tuning knob with high sensitivity(Minimum focusing accuracy:1micron)

Ø Objective lens turntable:6Hole position objective lens converter

Ø Mechanical stage: ceramic surface,Equipped with left-hand or right-hand low position drive device;Equipped with a rotating device and a torque adjustment device.

Ø Swinging spotlight:N.A. 0.9-0.16

Ø objective lense:PLN4XThePLN10XThePLN20XTheUPLFLN40XThePLN100XO

Ø Wide field of view three eye observation tube(Number of fields of view22），10XDiopter adjustable eyepiece

Ø 1Inch global shutter camera（SONYChip)

3. AlgaeAI 500 Fast counting algae based on deep learningAIsystem

Quick numbersAlgaeAI 500algaeAIThe automatic classification and counting system was developed by a team of senior experts. Based on in-depth research on algal characteristics and combined with machine learning theory, an innovative artificial intelligence algorithm model with high robustness was developed to achieve automatic classification and counting, size measurement, and biomass determination of algae.

Ø Automatically recognizable3～1000μmAlgae, including Chlorophyta, Cyanobacteria, Diatomia, Cryptophyta, Chlorella, Chrysophyta, Naked Algae, etc85Common algae and algae density detection range9.2×10² -10¹¹ cells/L

Ø Single field automatic recognition and analysis time≤In seconds, accurate algae identification, classification and counting can be achieved, and dominant algae sorting and biomass calculation can be synchronously completed.

Ø The identification rate of dominant species in the local classification recognition library≥90%The repeatability error of automatic analysis≤8%

Ø One click operation, full process dynamic visualization: main window images line up and move rapidly, algae cells are instantly recognized, and names are labeled in situ; Real time fluctuation updates of detection data (category, name, quantity, percentage, algal density, etc.); The green scrollbar displays the progress of image collection detection. Transparent operation throughout the process, facilitating quality monitoring. Mouse interaction allows for the addition, deletion, and modification of recognized species information, and real-time updates of sample analysis results.

Ø Sort the statistical data by dominant species, displaying the category, Chinese name, Latin name, number, proportion, and density of phytoplankton. Calculate the average single-cell length, width, height, diameter, area, and volume of each species, and automatically calculate biomass and total biomassShannonIndex, species evenness index, biodiversity index, abundance, dominance.

Ø Electronic Records, Data Traceability, and Reporting: Automatically save data and generate statistical reports with just one click. The completed analysis results can be saved, and the algae names can be marked in situ on the collected images. At any time, the statistical accuracy of each image can be reviewed again by opening the file.

Ø overlap/Adhesive algaeSeparation and identification: for algae cells that overlap highly together,AlgaeAI500Based on intelligent adhesion separation technology, it is possible to accurately capture individual algal cells from a pile of adhered cells.

Ø incomplete/Local algaeIntelligent recognition: for incomplete algal cells at the edge of the field of view,AlgaeAI500Based on intelligent morphological reasoning technology, it is possible to accurately identify which algae it is based on local information, thereby achieving leak free detection.

4. Configuration List

Ø Quick numbersAlgaeAI500Algae Intelligent Analysis System1set

Ø OlympusBX53A set of digital imaging system

Ø Data analysis workstation1Table: 12th generation Intel Core i9-12900 16 core, 32GB DDR4 memory, 4G discrete graphics card, 512GB solid-state drive, 4T hard drive, 27 inch display, Windows 10 operating system