Cyanobacteria Early Warning Instrument，which is based on machine vision and machine learning system, converts the shot of algae taken in monitored water into image signal by using machine vision device, and transmits it to the dedicated image processing system, to make algae measurement and monitor the concentration of algae in the monitored water in real time. This intuitive, fast and accurate contactless monitoring and measurement technology accommodates various operating environments and its emplacing manner is flexible. It can not only be fixed to the bracket, or fixed on mobile equipment, such as Unmanned aerial vehicle, Unmanned ship.

The fundation of algae identification system is using image sensor instead of naked eye to capture or collect the feature image of the target, and then distinguishing the state of the monitored object after computer image processing.

Image acquisition part is the prerequisite of algae identification detection, including image acquisition and transmission. Industrial camera obtains the image information of the measured object, and then digitizes the image information and transmits it to the computer, to form the digital image or data in the computer. Algae monitor uses ultra-high definition camera as image acquisition tool, and is equipped with the image recognition algorithm developed by our company, so it can accurately identify the concentration of algae in water. At the same time, we use artificial neural network algorithm and genetic algorithm to proceed machine learning and establish algae material transport control equation, which can generally accurately predict the probability of algae outbreak in the next 3 days. When algae outbreak exceeds the alert threshold, we will issue alert to the relevant water quality supervision departments.