Details

Autor(en) / Beteiligte

• Garcia, Ramon G.
• Balaba, Rowen Carlo M.
• Ebro, Euro Mark V.
• Ilagan, Rai Crescian D.

Titel

Detection and Classification of Pathogens in Gram-Stained Dairy Cow Milk Using Otsu Method

Ist Teil von

• 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2021, p.1-6

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE Xplore

Beschreibungen/Notizen

• Milk is one of the main ingredients used in dairy products. It is important to determine milk quality before distributing them to prevent consumers from catching illnesses such as vomiting and diarrhea. This research paper aims to create an automated microscope with an auto-focus and stitching feature, which will be used to capture images; and detect and classify whether pathogens, specifically Escherichia Coli, are present in the gram-stained milk sample. The researchers have automated the microscope stage slider and implemented the Variance of Laplacian method for the auto-focusing and Scale Invariant Feature Transform (SIFT) for the stitching. The image captured will then undergo Otsu thresholding and find Contour, which scans and analyzes whether Escherichia Coli is present in the gram-stained sample. After the images are analyzed, the result can be viewed using the GUI of the system. The data gathered during the experimentation process are tabulated in a confusion matrix to determine the false rejection rate and accuracy. After computing, the resulting false rejection rate is 11.6667 percent, and the resulting accuracy of the system is 88.3333 percent.