Identification of Algae with Pattern Recognition by Artificial Neural Network
Date Issued
2012
Date
2012
Author(s)
Yang, Ke
Abstract
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.
Subjects
phytoplankton
pattern recognition
Back-Propagation Neural Network
SDGs
Type
thesis
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