Colony Counting/Plankton Analysis Combination Instrument Algae Counting Instrument

Colony counting/plankton analysis combined instrument

Algacount M520Colony counting/plankton analysis combined instrumentIt is a multifunctional biological monitoring device upgraded by Xunshu Technology based on the M500 model in 2017. It integrates four functions: colony counting, intelligent identification and counting of planktonic animals, intelligent identification and counting of algae, and microscopic analysis. It is an intelligent image analysis tool specifically designed for monitoring freshwater and marine environmental organisms.

The newly designed software architecture integrates Xunshu Image Technology: "Progressive Similar Algae Search", "Confusing Algae Identification", "Typical Combination Lenovo Morphological Search", combined with exquisite plankton big data, to meet users' needs for rapid and intelligent identification, analysis, and counting of plankton. At the same time, it has multiple functions for analyzing plankton, such as multi-layer focusing, super field splicing, biomass analysis, counting of daughter cells in chain like bodies and gelatinous populations, and automatic counting of single-cell microalgae.

Equipped with a large field of view 1-inch SONY CMOS image sensor, it has fast shooting response speed and excellent color reproduction. The field of view range of a single shot is 5 times that of traditional cameras. In addition, MIC microscopic cell analysis software is provided to meet the precise observation and analysis needs of scientific research users.

Plankton analysis

Newly expanded plankton database

The fully expanded 2017 version of the phytoplankton database covers common freshwater algae in China's seven major water systems, 28 key lakes and reservoirs, as well as common marine algae around the East China Sea, Yellow Sea, Bohai Sea, and South China Sea. Redesign the search architecture and establish a four level search system consisting of "phylum", "order", "genus", and "species", which not only follows phytoplankton taxonomy but also takes into account the convenience of software interface development. Based on the principles of precision, intuitiveness, and practicality, this article focuses on editing and dividing the introduction of phytoplankton into columns, highlighting the characteristics of different species of phytoplankton, making their morphology, structure, reproduction, and ecology clear at a glance. Select a set of images that reflect the morphological characteristics of each specific algal species to avoid users being trapped in a large amount of redundant image information.

The zooplankton database consists of 24 major categories, including protozoan flagellates, protozoan meat footed insects, protozoan ciliates, rotifers, branchiopods, and copepods. It is displayed in both Chinese and Latin, with text introductions, hand drawn images, and a large number of microscopic photographs of zooplankton.

Multi mode intelligent search and identification

Progressive Similar Algae Search "is a revolutionary algae intelligent identification tool developed by Xunshu Technology. It adopts the Machine DeepFace technology and uses today's convolutional neural networks to extract deep features from any image. It can quickly and accurately detect the contours of unknown algal cells, extract feature information, match big data, and find a group of algae with the closest morphology from tens of thousands of image libraries, while prioritizing the display of common algae.

This feature has distinct characteristics and strong practicality, meeting both the efficiency of image search and the accuracy of graphic matching.

Easy to confuse algae identification ": Users can select 2, 4, 6 or more algae with similar morphology, and quickly compare them on the same interface. Through typical combination feature maps and summary text, they can quickly grasp the key differences between them and guide themselves to observe them from the differentiated detailed features.

Typical combination association "morphological retrieval: Based on morphological similarity and gradient, real typical algal cell images are selected, combined and classified, 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.

The features of graphic language, associative combination, feature selection, freshwater and ocean libraries, and easy browsing enable beginners to quickly master them.

Classification and counting of plankton, automatic sorting of dominant species

Plankton process counting: continuously obtain 200 field images, edit the counting table, click to mark different species, automatically accumulate the same genus and species in multiple fields, classify and count different species, accumulate the total number, automatically sort dominant species, and analyze the proportion of dominant groups.

Automatic counting of single-cell microalgae

The "dynamic automatic counting" system, which includes seven image segmentation algorithms, is suitable for rapid determination of cell concentration in pure cultured energy algae, medicinal or edible microalgae.

Microscopic measurement, biomass analysis

The system provides specialized microscopic analysis tools. The transparent digital ruler can achieve microscopic measurements at different objective magnifications; The biomass analysis module aggregates a large number of geometric models of algae and plankton, and automatically calculates their biomass through microscopic measurement data.

Data Security and Management

To ensure the authenticity and security of environmental monitoring data, the newly designed account management system can achieve multi account hierarchical management, granting administrators (laboratory leaders) the maximum authority to supervise and view the experimental data of different experimenters; And the experimenters cannot view or tamper with their respective data.

The statistical information of plankton is stored in electronic form to ensure the integrity of the data, which helps to standardize and paperless environmental monitoring, greatly improving work efficiency and in line with the development trend of laboratory testing. The statistical data is sorted by dominant species, including: total count, single-cell volume, percentage, algal density, abundance, dominance, total algal volume, Shannon index, species evenness index, carbon biomass, nitrogen biomass, and total carbon biomass.