AlgaeAI 700 plankton automatic classification and counting system

AlgaeAI 700 plankton automatic classification and counting system

The AlgaeAI 700 plankton automatic classification and counting system developed by Xunshu consists of the AlgaeAI system and a fully automatic scanning microscopy system. With the help of a high-speed three-axis electric platform, high-definition microscopic images can be obtained. Using AlgaeAI technology based on super deep learning, species identification, classification and counting, biomass measurement, and cell density calculation of plankton in water can be automatically performed. Data reports can be generated and exported automatically, achieving electronic recording of plankton and ensuring the integrity of electronic data. The AlgaeAI 700 is also equipped with an intelligent identification expert module for plankton, providing an important tool for studying plankton diversity in aquatic ecosystems.

Artificial intelligence, within reach

AlgaeAI 700 is an advanced intelligent analysis and recognition computing system for planktonic organisms based on Xunshu's ultra deep machine learning. It is a data analysis technology that can be used by personnel conducting planktonic analysis without the need for deep expertise in planktonic biology.

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

· Fast, single field analysis only takes 0.6 seconds

· Accurate and capable of identifying 145 common algae and 60 freshwater planktonic animals trained by neural networks, with a recognition accuracy of over 95%

· High robustness, suitable for complex microscopic images commonly seen in experiments: overlapping algal cells at high densities, local structural blurring caused by insufficient depth of field, background mixed with many impurities, cells only partially within the field of view.

Full transparency, visible reality

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.

· Click on 'AI Start', the main window images flash one by one, and the algae cells are framed one by one, with the name of the algae at the top of the box

· The green scrollbar on the upper right indicates that the sample is being tested

· 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 and fix it in 2-3 seconds.

Solving the difficulties of detecting complex field of view images

There are many challenges in the quantitative analysis process of phytoplankton, such as high suspended impurities in the collected quantitative water samples, high cell density after concentration, overlapping microalgae, existence of layered and divided fields of view of algal cells in the counting box, insufficient clarity of microscope optical imaging, inaccurate focusing

AlgaeAI 700 utilizes its super deep machine learning capabilities to quickly and reliably generate high-quality analysis and recognition results.

Not afraid of the crossing and overlapping of algae cells, it can be automatically untied directly

Samples with algae density exceeding 1010 cells/L and background clutter can still be recognized and counted

The light source and focus adjustment are not in place, and brittle rod algae and disk star algae are very shallow and light, but can still be recognized

Automatic identification of planktonic animals

The digital treasure trove of intelligent identification of algae

The grand algal image library covers freshwater and marine species in inland waters such as rivers, lakes, reservoirs and surrounding waters in China. The exquisite selected pictures and text introduction, combined with a rich retrieval framework, are helpers for the basic teaching of plankton and the popularization of algal knowledge by water environment monitoring organizations.

Taxonomic search, key editing and column introduction of common algae, with clear understanding of morphology, structure, reproduction, and ecology.

Morphological retrieval, based on morphological similarity and gradient, combines and classifies into graphic language, and combines the structural characteristics of cells or populations, such as flagella, pigment bodies, patterns, gelatinous covers, etc., to achieve accurate and fast morphological retrieval.

High quality microscopic scanning imaging

Using the Olympus research grade biological microscope BX43 as the optical imaging carrier, equipped with a smooth and quiet XYZ electric stage, achieving one click precise operation: automatic focusing, automatic scanning, and excellent image quality.