Posture-invariant hybrid scaling weight measurement algorithm for live eels

Qing Liu, Yuxing Han, Guoqi Yan, Jiasi Mo, Zishang Yang

Abstract


To obtain higher economic benefits, large eel breeding companies classify live eels by weight. Due to their strong mobility and smooth body surface, living eels are not suitable for traditional mechanical weight measurement. In this study, a live eel sorting machine based on machine vision was developed, and a novel method was developed for obtaining live eel weight measurements through images. First, a backlit workbench was designed to capture static images of eels, and then the projection area and skeleton length of the images were obtained by image preprocessing. For the eel's body shape, which is generally cylindrical and gradually transitions to a flat tail, the tail posture changes affect the shape of the images; thus, a weight measurement model combining the projected area and the skeleton length was proposed. The optimal scale division coefficient of the weight model was found to be 0.745 by experimentation. Then, select eels of different weight ranges were used for model error verification and to obtain the correction function of the error. The weight gradient was used to confirm the corrected eel weight model. Finally, the system calculation results were compared with the actual measurement results. The root mean square error (RMSE) was 12.94 g, and the mean absolute percentage error (MAPE) was 2.12%. The results show that the proposed method provided a convenient, fast, and low-cost non-contact weight measurement method for live eels, reduced the damage rate of live eels, and can meet the technical requirements of actual production.
Keywords: scale factor, weight measurement, non-contact, live eels
DOI: 10.25165/j.ijabe.20231602.7132

Citation: Liu Q, Han Y X, Yan G Q, Mo J S, Yang Z S. Posture-invariant hybrid scaling weight measurement algorithm for live eels. Int J Agric & Biol Eng, 2023; 16(2): 207-215.

Keywords


scale factor, weight measurement, non-contact, live eels

Full Text:

PDF

References


Cheng J-H, Dai Q, Sun D-W, Zheng X-A, Liu D, Pu H-B. Applications of non-destructive spectroscopic techniques for fish quality and safety evaluation and inspection. Trends in Food Science & Technology, 2013; 34(1): 18-31.

Koltes J E, Cole J B, Clemmens R, Dilger R N, Kramer L M, Lunney J K, et al. 2019. A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock. Frontiers in Genetics, 2019; 10: 1197. doi: c10.3389/fgene.2019.01197.

Liakos K G, Busato P, Moshou D, Pearson S, Bochtis D. Machine learning in agriculture: A review. Sensors, 2018; 18(8): 2674. doi: 10.3390/s18082674.

Ma X D, Zhu K X, Guan H O, Feng J R, Yu S, Liu G. High-throughput phenotyping analysis of potted soybean plants using colorized depth images based on a proximal platform. Remote Sensing, 2019; 11(9): 1085. doi: 10.3390/rs11091085.

Banerjee B P, Spangenberg G, Kant S. Fusion of spectral and structural information from aerial images for improved biomass estimation. Remote Sensing, 2020; 12(19): 3164. doi: 10.3390/rs12193164.

Niu Y X, Zhang L Y, Zhang H H, Han W T, Peng X S. Estimating above-ground biomass of maize using features derived from UAV-based RGB imagery. Remote Sensing, 2019; 11(11): 1261. doi: 10.3390/rs11111261.

Calixto R, Neto L G P, de Silveira Cavalcante T, Aragão M F, Silva E D. A computer vision model development for size and weight estimation of yellow melon in the Brazilian northeast. Scientia Horticulturae, 2019; 256: 108521. doi: 10.1016/j.scienta.2019.05.048.

Cheng H, Damerow L, Sun Y R, Blanke M. Early yield prediction using image analysis of apple fruit and tree canopy features with neural networks. Journal of Imaging, 2017; 3(1): 6. doi: 10.3390/jimaging3010006.

Rahman M M, Robson A, Bristow M. Exploring the potential of high resolution WorldView-3 imagery for estimating yield of mango. Remote Sensing, 2018; 10(12): 1866. doi: 10.3390/rs10121866.

Jung D-H, Park S H, Han X Z, Kim H-J. Image processing methods for measurement of lettuce fresh weight. Journal of Biosystems Engineering, 2015; 40(1): 89-93.

Koirala A, Walsh K B, Wang Z L, McCarthy C. Deep learning - Method overview and review of use for fruit detection and yield estimation. Computers and Electronics in Agriculture, 2019; 162: 219-234.

Zhang Z Q. Research on freshwater fish species identification and weight prediction based on machine vision. Master dissertation. Wuhan: Huazhong Agricultural University, 2011; 66p. (in Chinese)

Li L, Guo X Y. Research on fish classification method based on computer vision. Journal of Inner Mongolia Agricultural University (Natural Science Edition), 2015; 36(5): 120-124. (in Chinese)

Viazzi S, Van Hoestenberghe S, Goddeeris B M, Berckmans D. Automatic mass estimation of Jade perch Scortum barcoo by computer vision. Aquacultural Engineering, 2014; 64: 42-48.

Ma G Q, Tian Y C, Li X L. Individual weight estimation of narrow-bodied tongue sole based on irregular image area measurement. Microcomputers and Applications, 2016; 35(16): 67-71. (in Chinese)

Wang L, Sun C H, Li W Y, Ji Z T, Zhang X, Wang Y Z, et al. Establishment of broiler quality estimation model based on depth image and BP neural network. Thransactions of the CSAE, 2017; 33(13): 199-205. (in Chinese)

De Wet L, Vranken E, Chedad A, Aerts J M, Ceunen J, Berckmans D. Computer-assisted image analysis to quantify daily growth rates of broiler chickens. British Poultry Science, 2003; 44(4): 524-532.

Schofield C P, Marchant J A, White R P, Brandl N, Wilson M. Monitoring pig growth using a prototype imaging system. Journal of Agricultural Engineering Research, 1999; 72(3): 205-210.

Yang Y, Teng G H, Li B M, Shi Z X. Measurement of pig weight based on computer vision. Transactions of the CSAE, 2006; 22(2): 127-131. (in Chinese)

Fu W S, Teng G H, Yang Y. Research on three-dimensional model of pig’s weight estimating. Transactions of the CSAE, 2006; 22(S2): 84-87. (in Chinese)

Alikhanov J, Penchev S M, Georgieva T D, Modazhanov A, Shynybay Z, Daskalov P I. An indirect approach for egg weight sorting using image processing. Journal Of Food Measurement And Characterization, 2018; 12: 87-93.

Wang J F, Luo X W, Hong T S, Ge Z Y. Application of computer vision technology in detecting mango weight and surface bruise. Transactions of the CSAE, 1998(4):186-189. (in Chinese)

Kalantar A, Edan Y, Gur A., Klapp, l. A deep learning system for single and overall weight estimation of melons using unmanned aerial vehicle images. Comput Electron Agr, 2020; 178.

Dowlati M, Mohtasbi S S, de la Guardia M. Application of machine-vision techniques to fish-quality assessment. TrAC Trends in Analytical Chemistry, 2012; 40: 168-179.

Dutta M K, Issac A, Minhas N. Image processing based method to assess fish quality and freshness. Journal of Food Engineering, 2016; 177: 50-58.

Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems Man & Cybernetics, 2007; 9(1): 62-66.

Liu G L, Reda F A, Shih K J, Wang T-C, Tao A, Catamzaro B. Image inpainting for irregular holes using partial convolutions. In: Computer Vision – ECCV 2018, Springer, 2018; pp.89-105. doi: 10.1007/978-3-030-01252-6_6.




Copyright (c) 2023 International Journal of Agricultural and Biological Engineering

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

2023-2026 Copyright IJABE Editing and Publishing Office