Internet of Things for Underwater Shrimp Image Detection Using Blob Detector

Arif Setiawan (1), Hadiyanto Hadiyanto (2), Catur Edi Widodo (3)
(1) Doctoral Program of Information System, School of Postgraduate Studies, Diponegoro University, Semarang, 50241, Indonesia
(2) Center of Biomass and Renewable Energy (CBIORE), Department of Chemical Engineering, Diponegoro University. Tembalang, Semarang, 50271, Indonesia
(3) Department of Physics, Faculty of Science and Mathematics, Diponegoro University, Tembalang, Semarang, 50275, Indonesia
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How to cite (IJASEIT) :
Setiawan, Arif, et al. “Internet of Things for Underwater Shrimp Image Detection Using Blob Detector”. International Journal on Advanced Science, Engineering and Information Technology, vol. 13, no. 3, May 2023, pp. 954-60, doi:10.18517/ijaseit.13.3.17470.
Measuring biomass content is an important stage in harvesting shrimp as it will determine the harvest time. Manual detection has caused shrimp stress and eventually caused shrimp death; therefore, a new shrimp biomass determination is required. This research aims to design an IoT technique-based biomass measurement, using underwater shrimp video with fog and cloud computing processes to easily detect shrimp underwater, irrespective of the complex noise. The method consists of several steps: image processing using grayscale, thresholding, contour edge detection, labeling, and blob detection. The results revealed that the highest SSIM value in the thresholding process was 0.18, while the lowest MSE was 91.35. In addition, in the contour edge detection process, the highest PSNR value was 3.6, and the lowest MSE was 2.06. The blob detection process produces a maximum key performance of 566, 411, and 387 in the Laplacian of Gaussian (LoG), Difference of Gaussian (DoG), and Determinants of Hessian (DoH) methods, respectively. The Quality of Service (QoS) obtained throughput, loss, and delay values of 832.25, 0%, and 7.25 ms, respectively, in the data acquisition and computation processes, with the three parameters at a very good level. In conclusion, the IoT model is very suitable for underwater shrimp detection because it is a non-invasive method, contains high key performance blob detection, and has a very good QoS level and high-speed computation process.

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