AlgaeAI 600 Algae Artificial Intelligence Analyzer

Quick numbersAlgaeAI 600Algae artificial intelligence analyzer, consisting of scientific grade biological microscope, industrial digital cameraHigh precision electric platform andAlgaeAIComposition of artificial intelligence systems. CanQuick automatic scanning and photography, 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.

AlgaeAI 600It is also equipped with an intelligent identification expert module for plankton, providing an important tool for the study of plankton diversity in aquatic ecosystems.

Artificial intelligence, within reach

AlgaeAI 600Algae using improved 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 view analysis1.2second

l Accurate and recognizable, trained by neural networks130Multiple common algae with high recognition accuracy92%above

l Adapt to common complex microscopic images in experiments: overlapping algal cells at higher densities, local structural blurring caused by insufficient depth of field, and cells only partially within the field of view

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 difficult microscopic images

There are many challenges in the quantitative analysis of phytoplankton, such as insufficient focusing of microscope optical imaging, overlapping of microalgae when cell density is high, and only partial presence of algal cell structure in the field of view……

AlgaeAI 600By utilizing its deep machine learning capabilities, high-quality analysis and recognition results can be generated quickly and reliably.

Larger field of view（20Multiple magnification) for identifying and counting algae with significant differences in body size due to the presence of impurities

Easily identify and distinguish algae in situations where there are numerous cells, fuzzy or overlapping

Algae cells are distributed within the shadow of the dividing line in the counting box and can still be detected

Intelligent identification of plankton

Plankton Expert Database: Composed of exquisite color micrographs, hand drawn images, bilingual displays in Chinese and Latin, it forms freshwater and marine plankton pools that can be accessed by“Door, order, genus, species”Four level expansion search. Among them, algae15A door1700Individual genus; zooplankton26Major categories2000Individual belonging. Freshwater algae covering the plain lake area in eastern China, the Yunnan-Guizhou Plateau lake area, Northeast lake area, Qinghai Tibet Plateau lake area, Mongolian Xinjiang Plateau lake area and seven major water systems, as well as marine algae around the East China Sea, Yellow Sea, Bohai Sea and South China Sea.

Morphological retrievalBased on morphological similarity and gradient, it is combined and classified into graphic language, and combined with the structural characteristics of cells or populations, such as flagella, pigment bodies, patterns, gelatinous covers, etc., to achieve accurate and fast morphological retrieval.

Identification of easily confused algae, choose4、6Quickly compare one or more algae with similar morphology on the same interface, and use typical combination feature maps and summary text to quickly grasp the key differences between them, guiding oneself to focus on observing the differentiated detailed features.

High speed microscopic scanning imaging

Scanning system with OlympusBX43The research grade biological microscope is equipped with a smooth and quiet frameXYZElectric loading platform, capable of loading at once4Film, achieving one click precise operation: autofocus, automatic scanning, excellent image quality.

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. Fully automatic scanning microscopy system

Ø Rack: OlympusBX43Microscope;4、10、40Double flat field achromatic objective lens20Double flat field apochromatic objective lens

Ø High precision electronic controlXYZAutomatic scanning platform: implementationX/Y/ZMicro scale axial motion and automatic control

Ø stepper motorXYPlatform: Minimum Step Size≤ 0.1umBi directional repetitive positioning accuracy≤±1umMaximum speed:20mm/s

Ø According to the adjusted density of phytoplankton in the sample, imaging can be performed using methods such as whole sheet scanning, grid scanning, and random field scanning

Ø electricZAxis: Closed loop resolution0.156umRepetitive positioning accuracy:≤±0.4um

Ø High sensitivity global shutter camera, multi depth continuous automatic scanning focus, adjustable shooting layer spacing, image resolution<0.20um/pixel

3. AlgaeAI 600 Fast counting algae based on deep learningAIsystem

Quick numbersAlgaeAI 600algaeAIThe automatic classification and counting system, developed by a team of senior experts, is based on in-depth research on algal characteristics and combined with improved deep machine learning theory to innovatively establish a robust artificial intelligence algorithm model, achieving automatic classification and counting, size measurement, and biomass determination of algae.

Ø Automatically recognizable3～1000μmAlgae, including Chlorophyta, Cyanobacteria, Diatomia, Cryptophyta, Chlorella, Chrysophyta, Naked Algae, etc130Common algae and algae density detection range9.2×10² -10¹¹ cells/L

Ø Single field automatic recognition and analysis time ≤ 1.2 seconds, achieving accurate algae recognition, classification and counting, and synchronously completing dominant algae sorting and biomass calculation.

Ø The identification rate of dominant species in the local classification recognition library≥92%The repeatability error of automatic analysis≤6%

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,AlgaeAI 600Based 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,AlgaeAI 600Based on intelligent morphological reasoning technology, it is possible to accurately identify which algae it is based on local information, thereby achieving leak free detection.

Ø Fuzzy cellCalculation recognition: for algae cells that appear light and unclear in the field of view due to insufficient depth of focus,AlgaeAI 600Based on fuzzy inference technology, it is possible to accurately analyze and identify which algal cell it is.

4. Qualitative analysis and intelligent identification module for planktonic organisms

Ø Plankton Expert Database: Composed of exquisite color micrographs, hand drawn images, bilingual displays in Chinese and Latin, it forms freshwater and marine plankton pools that can be accessed by“Door, order, genus, species”Four level expansion search. among whichalgae15A door1700Individual genus; zooplankton26Major categories2000Individual belonging.Freshwater algae covering the plain lake area in eastern China, the Yunnan-Guizhou Plateau lake area, Northeast lake area, Qinghai Tibet Plateau lake area, Mongolian Xinjiang Plateau lake area and seven major water systems, as well as marine algae around the East China Sea, Yellow Sea, Bohai Sea and South China Sea

Ø Typical combination association "morphological retrieval: using graphic language, combination association, and combining the structural characteristics of cells or populations to achieve accurate and fast morphological retrieval. Equipped with features such as multiple selection, freshwater and ocean storage, and easy browsing, beginners can quickly master them.

Ø Multidimensional progressive similarity algae search and identification: an automatic and intelligent algae cell graphic recognition tool,3-5Instant implementation: detecting unknown algal cell contours, extracting feature information, matching big data, accurately identifying possible algae with similar morphology, and synchronously displaying the most similar common algae through the "priority selection" option.

Ø Identification of easily confused algae: Designed for inexperienced experimenters, multiple algae that are easily confused due to their similar morphology are screened and quickly compared on the same interface. Through typical feature puzzles and summary text, the distinguishing points are quickly grasped.

5. Configuration List

Ø Quick numbersAlgaeAI 600Algae Intelligent Analysis System1set

Ø fully automaticDigital Microscopic Imaging Scanning System1Set:Rack OlympusBX43Microscope,4XThe10XThe40XFlat field achromatic objective lens20XFlat field apochromatic objective lens10Adjustable diopter eyepiece, trinocular observation tube5Hole position objective lens converter,4High precision electronic control for sheet fluxXYZAutomatic scanning platform and control box, high-sensitivity global scanning camera

Ø Data analysis workstation1Stage: The12Intelligent Intel Corei9-12900 16Nuclear,32G DDR4Memory,4GIndependent graphics card,512GSolid state drives,4THard drive,27Inch display,Windows 10Professional version operating system

6. service

Ø New machine on-site installation, debugging, and training

Ø Build a free local database algorithm for users once

Ø Long term provision of remote assistance and guidance servicesAssist in identifying complex samples