Identification of Algae with Pattern Recognition by Artificial Neural Network
|關鍵字:||藻類;影像辨識;倒傳遞類神經網路;phytoplankton;pattern recognition;Back-Propagation Neural Network||公開日期:||2012||摘要:||水庫優養化是造成水質惡化之主要原因之一，本研究欲建立一節省時間及人
Eutrophication is the most common cause for the deterioration of reservoir water quality in Taiwan. Identification of algal species and estimation of the abundance are necessary for the warming and managing the situation of eutrophication. The aim of this research is to establish an automatic algae recognition system which is not only to recognize the species of phytoplankton in natural water sample but also to reduce the time cost and labor. This research could be divided into two parts, the hardware development and the software coding. The hardware includes a sample injector followed by the automatic condensation equipment, the flowing cell, a microscope and a CCD. The designing of the automatic condensation equipment was based on the tangential flow filtration principle. The water sample was driven by Ismatec Peristaltic pump, into the automatic condensation equipment which operated smoothly without backwash. According the concept of to Flow Cytometry, this research devised a shallow flow trough cell called flowing cell. The high speed CCD would capture the digital image continuously while water sample passing through this flowing cell. This approach reduced the material and time cost of making glass coverslips. The software coding was composed of image pre-treatment and image recognition. We wrote an automatic algae recognition program by Matlab language and trained the Back-Propagation Neural Network model by inputing extracted configuration features and color features of the training pictures. The trained Back-Propagation Neural Network is able to recognize unknown algae cells and colonies. The recognition accuracy for a mixture of four artificial cultivated algal species was 87% for Chlamydomonas, 87% for Cyanobacteri, 93% for Melosira granulate IV and 93% for Microcystaceae. In addition, the system recognition accuracy was 70%. For Merismopedia, 50% for Monoraphidium Contortum, 73% for Staurastrum and % for Pediastrum refer in a natural water sample. As a result, the system recognition accuracy for artificial cultivated algae species was higher. If we want to apply this monitoring system to natural water body in the future, we should input specific algae images in the water body to train the Neural Network model. The Back-Propagation Neural Network would self-adjust and self-learn. To develop a effective tool for monitoring phytoplanktons in natural waters.
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