Internet of Things for Underwater Shrimp Image Detection Using Blob Detector

Arif Setiawan, Hadiyanto Hadiyanto, Catur Edi Widodo


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.


Shrimp underwater; image detection; blob detector; key performance; IoT

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X. Dong and V. Raghavan, “Trends in Food Science & Technology Recent advances of selected novel processing techniques on shrimp allergenicity : A review,†Trends Food Sci. Technol., vol. 124, no. April, pp. 334–344, 2022, doi: 10.1016/j.tifs.2022.04.024.

S. Ray et al., “Role of shrimp farming in socio-economic elevation and professional satisfaction in coastal communities of Southern Bangladesh,†Aquac. Reports, vol. 20, no. June 2020, p. 100708, 2021, doi: 10.1016/j.aqrep.2021.100708.

C. N. Udanor et al., “An internet of things labelled dataset for aquaponics fish pond water quality monitoring system,†Data Br., vol. 43, p. 108400, 2022, doi: 10.1016/j.dib.2022.108400.

Z. Liu, X. Jia, and X. Xu, “Study of shrimp recognition methods using smart networks,†Comput. Electron. Agric., vol. 165, Oct. 2019, doi: 10.1016/j.compag.2019.104926.

J. C. Ovalle, C. Vilas, and L. T. Antelo, “On the use of deep learning for fish species recognition and quantification on board fishing vessels,†Mar. Policy, vol. 139, no. August 2021, p. 105015, 2022, doi: 10.1016/j.marpol.2022.105015.

W. Xu et al., “Shadow detection and removal in apple image segmentation under natural light conditions using an ultrametric contour map,†Biosyst. Eng., vol. 184, pp. 142–154, 2019, doi: 10.1016/j.biosystemseng.2019.06.016.

X. Wu, L. Bi, M. Fulham, D. Dagan, L. Zhou, and J. Kim, “Neurocomputing Unsupervised brain tumor segmentation using a symmetric-driven adversarial network,†Neurocomputing, vol. 455, pp. 242–254, 2021, doi: 10.1016/j.neucom.2021.05.073.

M. Calkovský et al., “Materials Characterization Comparison of segmentation algorithms for FIB-SEM tomography of porous polymers : Importance of image contrast for machine learning segmentation,†vol. 171, no. June 2020, 2021, doi: 10.1016/j.matchar.2020.110806.

M. Garrett et al., “Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images : Dosimetric validation and reader study based clinical acceptability testing,†Radiother. Oncol., vol. 165, pp. 52–59, 2021, doi: 10.1016/j.radonc.2021.10.008.

Q. Hu et al., “A method for measuring ice thickness of wind turbine blades based on edge detection,†Cold Reg. Sci. Technol., vol. 192, no. March, p. 103398, 2021, doi: 10.1016/j.coldregions.2021.103398.

G. Wang, C. Lopez-Molina, and B. De Baets, “Automated blob detection using iterative Laplacian of Gaussian filtering and unilateral second-order Gaussian kernels,†Digit. Signal Process. A Rev. J., vol. 96, p. 102592, 2020, doi: 10.1016/j.dsp.2019.102592.

M. M. Rashid, A. A. Nayan, M. O. Rahman, S. A. Simi, J. Saha, and M. G. Kibria, “IoT based Smart Water Quality Prediction for Biofloc Aquaculture,†Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 6, pp. 56–62, 2021, doi: 10.14569/IJACSA.2021.0120608.

J. Singh, P. Singh, and S. Singh, “Journal of Parallel and Distributed Computing Fog computing : A taxonomy , systematic review , current trends and research challenges,†J. Parallel Distrib. Comput., vol. 157, pp. 56–85, 2021, doi: 10.1016/j.jpdc.2021.06.005.

S. Suryono, A. Khuriati, and T. Mantoro, “A fuzzy rule-based fog–cloud computing for solar panel disturbance investigation,†Cogent Eng., vol. 6, no. 1, pp. 1–19, 2019, doi: 10.1080/23311916.2019.1624287.

O. Li and P. lang Shui, “Subpixel blob localization and shape estimation by gradient search in parameter space of anisotropic Gaussian kernels,†Signal Processing, vol. 171, 2020, doi: 10.1016/j.sigpro.2020.107495.

K. Malinowski and K. Saeed, “An iris segmentation using harmony search algorithm and fast circle fitting with blob detection,†Biocybern. Biomed. Eng., vol. 42, no. 1, pp. 391–403, 2022, doi: 10.1016/j.bbe.2022.02.010.

J. D. Trivedi, S. D. Mandalapu, and D. H. Dave, “Vision-based real-time vehicle detection and vehicle speed measurement using morphology and binary logical operation,†J. Ind. Inf. Integr., vol. 27, no. August 2021, p. 100280, 2022, doi: 10.1016/j.jii.2021.100280.

D. N. Triwibowo, E. Utami, and S. Sukoco, “Analisis BLOB Detection Pada Pendeteksian dan Perhitungan Kendaraan di Jalan Tol,†Inspir. J. Teknol. Inf. dan Komun., vol. 10, no. 1, p. 1, 2020, doi: 10.35585/inspir.v10i1.2532.

P. Niyishaka and C. Bhagvati, “Copy-move forgery detection using image blobs and BRISK feature,†Multimed. Tools Appl., no. September, 2020, doi: 10.1007/s11042-020-09225-6.

C. Anitha and R. M. S. Kumar, “GEVE : A generative adversarial network for extremely dark image / video enhancement,†Pattern Recognit. Lett., vol. 155, pp. 159–164, 2022, doi: 10.1016/j.patrec.2021.10.030.

A. F. A. Fernandes et al., “Deep Learning image segmentation for extraction of fish body measurements and prediction of body weight and carcass traits in Nile tilapia,†Comput. Electron. Agric., vol. 170, no. November 2019, p. 105274, 2020, doi: 10.1016/j.compag.2020.105274.

Y. Song, S. Ren, Y. Lu, X. Fu, and K. K. L. Wong, “Computer Methods and Programs in Biomedicine Deep learning-based automatic segmentation of images in cardiac radiography : A promising challenge,†vol. 220, 2022, doi: 10.1016/j.cmpb.2022.106821.

G. Li, S. Ma, H. Li, F. Liu, and L. Lin, “‘“ Terrace compression method â€â€™ and its application in heterogeneity contour detection of transmission images,†vol. 514, no. December 2021, pp. 1–8, 2022, doi: 10.1016/j.optcom.2022.128114.

X. Hu and Y. Wang, “Catena Monitoring coastline variations in the Pearl River Estuary from 1978 to 2018 by integrating Canny edge detection and Otsu methods using long time series Landsat dataset,†vol. 209, no. November 2021, 2022.

R. Priyadharsini and T. S. Sharmila, “Object Detection in Underwater Acoustic Images Using Edge Based Segmentation Method,†Procedia Comput. Sci., vol. 165, pp. 759–765, 2019, doi: 10.1016/j.procs.2020.01.015.

P. Gao, Y. Song, M. Song, P. Qian, and Y. Su, “Scripta Materialia Extract nanoporous gold ligaments from SEM images by combining fully convolutional network and Sobel operator edge detection algorithm,†Scr. Mater., vol. 213, no. November 2021, p. 114627, 2022, doi: 10.1016/j.scriptamat.2022.114627.

R. A. A. S and S. Gopalan, “ScienceDirect Comparative Comparative Analysis Analysis of of Eight Eight Direction Direction Sobel Sobel Edge Edge Detection Detection Algorithm for Brain MRI Images Algorithm for Brain Tumor MRI Images,†Procedia Comput. Sci., vol. 201, pp. 487–494, 2022, doi: 10.1016/j.procs.2022.03.063.

M. Huang, Y. Liu, and Y. Yang, “Edge detection of ore and rock on the surface of explosion pile based on improved Canny operator,†Alexandria Eng. J., vol. 61, no. 12, pp. 10769–10777, 2022, doi: 10.1016/j.aej.2022.04.019.

Y. Xu, B. Sun, X. Yan, J. Hu, and M. Chen, “Multi-focus image fusion using learning based matting with sum of the Gaussian-based modified Laplacian,†Digit. Signal Process., vol. 106, p. 102821, 2020, doi: 10.1016/j.dsp.2020.102821.

D. Zhang, H. Ma, and L. Pan, “A gamma-signal-regulated connected components labeling algorithm,†Pattern Recognit., vol. 91, pp. 281–290, 2019, doi: 10.1016/j.patcog.2019.02.022.

C. Ricotta and S. Pavoine, “A new parametric measure of functional dissimilarity : Bridging the gap between the Bray-Curtis dissimilarity and the Euclidean distance,†Ecol. Modell., vol. 466, no. December 2021, p. 109880, 2022, doi: 10.1016/j.ecolmodel.2022.109880.

S. Wang, T. B. Eldred, J. G. Smith, and W. Gao, “Ultramicroscopy AutoDisk : Automated diffraction processing and strain mapping in,†Ultramicroscopy, vol. 236, no. March, p. 113513, 2022, doi: 10.1016/j.ultramic.2022.113513.

M. Zhang, X. Wang, H. Feng, Q. Huang, X. Xiao, and X. Zhang, “Wearable Internet of Things enabled precision livestock farming in smart farms: A review of technical solutions for precise perception, biocompatibility, and sustainability monitoring,†J. Clean. Prod., vol. 312, no. May, p. 127712, 2021, doi: 10.1016/j.jclepro.2021.127712.

J. Wan, J. Li, Q. Hua, A. Celesti, and Z. Wang, “Intelligent equipment design assisted by Cognitive Internet of Things and industrial big data,†Neural Comput. Appl., vol. 32, no. 9, pp. 4463–4472, May 2020, doi: 10.1007/s00521-018-3725-5.

M. I. Ghozali, W. H. Sugiharto, H. Susanto, M. A. Budihardjo, and S. Suryono, “Measurement performance quality of services (QoS) to optimizing on wireless sensor network topology for water pollution monitoring system,†J. Phys. Conf. Ser., vol. 1943, no. 1, 2021, doi: 10.1088/1742-6596/1943/1/012019.

Y. Han, T. Song, J. Feng, and Y. Xie, “Grayscale-inversion and rotation invariant image description with sorted LBP features,†Signal Process. Image Commun., vol. 99, no. June, p. 116491, 2021, doi: 10.1016/j.image.2021.116491.

U. Sara, M. Akter, and M. S. Uddin, “Image Quality Assessment through FSIM , SSIM , MSE and PSNR — A Comparative Study,†pp. 8–18, 2019, doi: 10.4236/jcc.2019.73002.

N. A. Gard, C. Bunker, and A. Yilmaz, “A spacetime model for one-shot active contour extraction scheme for human detection in image sequences,†Comput. Vis. Image Underst., vol. 202, no. September 2020, p. 103113, 2021, doi: 10.1016/j.cviu.2020.103113.

S. Wu, J. Yang, X. Wang, and X. Li, “IoU-Balanced loss functions for single-stage object detection,†Pattern Recognit. Lett., vol. 156, pp. 96–103, 2022, doi: 10.1016/j.patrec.2022.01.021.

C. Zheng et al., “A mango picking vision algorithm on instance segmentation and key point detection from RGB images in an open orchard,†Biosyst. Eng., vol. 206, pp. 32–54, 2021, doi: 10.1016/j.biosystemseng.2021.03.012.

S. Pallewatta, V. Kostakos, and R. Buyya, “QoS-aware placement of microservices-based IoT applications in Fog computing environments,†Futur. Gener. Comput. Syst., vol. 131, pp. 121–136, 2022, doi: 10.1016/j.future.2022.01.012.

N. Ben Salah and N. Bellamine Ben Saoud, “Adaptive data placement in the Fog infrastructure of IoT applications with dynamic changes,†Simul. Model. Pract. Theory, vol. 119, no. April, p. 102557, 2022, doi: 10.1016/j.simpat.2022.102557.



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