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Volume 48 Number 5
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Volume 48 Number 4 Volume 48 Number 5

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Research articles

ScienceAsia 48 (2022): 614-622 |doi: 10.2306/scienceasia1513-1874.2022.090

Automated molting detection system for commercial soft-shell crab (Portunus pelagicus) production

Sukkrit Nimitkula,*, Krisada Phromsuthirakb, Wara Taparhudeea, Vutipong Areekulc, Vutthichai Oniamd, Wasana Arkronratd

ABSTRACT:     Cost, availability and reliability of labor are major problems in any aquaculture operation, and particularly in the soft-shell crab industry, since its success depends on the precise timing and accuracy of the workers to observe and harvest the newly molted crabs before their shells harden. To improve efficiency and reduce the dependence on human intervention, we have developed an automated molting detection system. The detection system utilizes the fact that the crab?s carapace reflects infrared light (appearing as white pixels) much more strongly than the surrounding areas. By using Internet Protocol (IP) cameras, network video recorder (NVR), personal computer and newly designed image analysis software, molting can be detected by continuously measuring relative changes in white pixel area. Twodimensional Gaussian function and Otsu?s thresholding method are incorporated into the detection software. Snapshot images, date, and time of molting are recorded automatically and displayed via user interface. Test results indicated that the highest (100%) hit rate and lowest precision (13.92%) were obtained when detection threshold was 20%. Lower hit rates and higher precision were observed at higher threshold levels. The optimum threshold for detecting molting in commercial operations is discussed.

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a Department of Aquaculture, Faculty of Fisheries, Kasetsart University, Bangkok 10900 Thailand
b Faculty of Digital Technology, Chitralada Technology Institute, Bangkok 10300 Thailand
c Department of Electrical Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900 Thailand
d Klongwan Fisheries Research Station, Faculty of Fisheries, Kasetsart University, Prachuap Khiri Khan 77000 Thailand

* Corresponding author, E-mail: ffisskn@ku.ac.th

Received 7 Apr 2021, Accepted 3 Mar 2022