Application of the convolutional network Mask R-CNN for the estimation of the body weight of the guinea pig

Authors

DOI:

https://doi.org/10.51252/rcsi.v4i1.614

Keywords:

mobile application, image dataset, artificial intelligence, convolutional networks

Abstract

Artificial intelligence can contribute in tracking the productive cycle of the cuy through the application of convolutional networks, being a necessity the estimation of its weight. This study focused on the application of the Mask R-CNN convolutional network, using a mobile application as a tool for image capture. The methodology covered the following stages: i) bibliographic review, ii) data collection (images and pig weights), iii) image processing through data augmentation, iv) construction of a dataset (image selection and data transformation); , v) adaptation and training of the convolutional network, vi) analysis of the results to validate its performance, and finally, vii) implementation of a mobile application as a weight estimation tool. A set of 6244 pig images with their respective weights and masks was managed to be collected, together with the Mask R-CNN network adaptation. These tasks led to a correlation of R2 = 80.2% with the validation set, as well as to the development of a functional prototype capable of estimating the weight of pigs using the camera of a cell phone.

References

Abdulla, W. (2017). Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. GitHub. https://github.com/matterport/Mask_RCNN

Buayai, P., Piewthongngam, K., Leung, C. K., & Saikaew, K. R. (2019). Semi-Automatic Pig Weight Estimation Using Digital Image Analysis. Applied Engineering in Agriculture, 35(4), 521–534. https://doi.org/10.13031/aea.13084 DOI: https://doi.org/10.13031/aea.13084

Cang, Y., He, H., & Qiao, Y. (2019). An Intelligent Pig Weights Estimate Method Based on Deep Learning in Sow Stall Environments. IEEE Access, 7, 164867–164875. https://doi.org/10.1109/ACCESS.2019.2953099 DOI: https://doi.org/10.1109/ACCESS.2019.2953099

Cominotte, A., Fernandes, A. F. A., Dorea, J. R. R., Rosa, G. J. M., Ladeira, M. M., van Cleef, E. H. C. B., Pereira, G. L., Baldassini, W. A., & Machado Neto, O. R. (2020). Automated computer vision system to predict body weight and average daily gain in beef cattle during growing and finishing phases. Livestock Science, 232, 103904. https://doi.org/10.1016/j.livsci.2019.103904 DOI: https://doi.org/10.1016/j.livsci.2019.103904

Dohmen, R., Catal, C., & Liu, Q. (2022). Computer vision-based weight estimation of livestock: a systematic literature review. New Zealand Journal of Agricultural Research, 65(2–3), 227–247. https://doi.org/10.1080/00288233.2021.1876107 DOI: https://doi.org/10.1080/00288233.2021.1876107

Fernandes, A. F. A., Dórea, J. R. R., Fitzgerald, R., Herring, W., & Rosa, G. J. M. (2019). A novel automated system to acquire biometric and morphological measurements and predict body weight of pigs via 3D computer vision1. Journal of Animal Science, 97(1), 496–508. https://doi.org/10.1093/jas/sky418 DOI: https://doi.org/10.1093/jas/sky418

Gil Santos, V. (2007). Importancia del Cuy y su Competitividad en el Mercado. Archivos Latinoamericanos De Producción Animal, 15(5). https://ojs.alpa.uy/index.php/ojs_files/article/view/2741

González Marcos, A., Martínez de Pisón Ascacíbar, F., Pernía Espinoza, A., Alba Elías, F., Castejón Limas, M., Ordieres Meré, J., & Vergara González, E. (2006). Técnicas y Algoritmos Básicos de Visión Artificial (1st ed.). Universidad de La Rioja.

He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV), 2980–2988. https://doi.org/10.1109/ICCV.2017.322 DOI: https://doi.org/10.1109/ICCV.2017.322

Jensen, D., & Dominiak, K. (2018). Automatic estimation of slaughter pig live weight using convolutional neural networks. II International Conference on Agro BigData and Decision Support Systems in Agriculture.

Jun, K., Kim, S. J., & Ji, H. W. (2018). Estimating pig weights from images without constraint on posture and illumination. Computers and Electronics in Agriculture, 153, 169–176. https://doi.org/10.1016/j.compag.2018.08.006 DOI: https://doi.org/10.1016/j.compag.2018.08.006

Kashiha, M., Bahr, C., Ott, S., Moons, C. P. H., Niewold, T. A., Ödberg, F. O., & Berckmans, D. (2014). Automatic weight estimation of individual pigs using image analysis. Computers and Electronics in Agriculture, 107, 38–44. https://doi.org/10.1016/j.compag.2014.06.003 DOI: https://doi.org/10.1016/j.compag.2014.06.003

Konovalov, D. A., Saleh, A., Efremova, D. B., Domingos, J. A., & Jerry, D. R. (2019). Automatic Weight Estimation of Harvested Fish from Images. 2019 Digital Image Computing: Techniques and Applications (DICTA), 1–7. https://doi.org/10.1109/DICTA47822.2019.8945971 DOI: https://doi.org/10.1109/DICTA47822.2019.8945971

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft COCO: Common Objects in Context (pp. 740–755). https://doi.org/10.1007/978-3-319-10602-1_48 DOI: https://doi.org/10.1007/978-3-319-10602-1_48

Ma, W., Li, Q., Li, J., Ding, L., & Yu, Q. (2021). A method for weighing broiler chickens using improved amplitude-limiting filtering algorithm and BP neural networks. Information Processing in Agriculture, 8(2), 299–309. https://doi.org/10.1016/j.inpa.2020.07.001 DOI: https://doi.org/10.1016/j.inpa.2020.07.001

Miller, G. A., Hyslop, J. J., Barclay, D., Edwards, A., Thomson, W., & Duthie, C.-A. (2019). Using 3D Imaging and Machine Learning to Predict Liveweight and Carcass Characteristics of Live Finishing Beef Cattle. Frontiers in Sustainable Food Systems, 3. https://doi.org/10.3389/fsufs.2019.00030 DOI: https://doi.org/10.3389/fsufs.2019.00030

Mortensen, A. K., Lisouski, P., & Ahrendt, P. (2016). Weight prediction of broiler chickens using 3D computer vision. Computers and Electronics in Agriculture, 123, 319–326. https://doi.org/10.1016/j.compag.2016.03.011 DOI: https://doi.org/10.1016/j.compag.2016.03.011

Pezzuolo, A., Guarino, M., Sartori, L., González, L. A., & Marinello, F. (2018). On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera. Computers and Electronics in Agriculture, 148, 29–36. https://doi.org/10.1016/j.compag.2018.03.003 DOI: https://doi.org/10.1016/j.compag.2018.03.003

Qiao, Y., Truman, M., & Sukkarieh, S. (2019). Cattle segmentation and contour extraction based on Mask R-CNN for precision livestock farming. Computers and Electronics in Agriculture, 165, 104958. https://doi.org/10.1016/j.compag.2019.104958 DOI: https://doi.org/10.1016/j.compag.2019.104958

Wang, Y., Yang, W., Winter, P., & Walker, L. (2008). Walk-through weighing of pigs using machine vision and an artificial neural network. Biosystems Engineering, 100(1), 117–125. https://doi.org/10.1016/j.biosystemseng.2007.08.008 DOI: https://doi.org/10.1016/j.biosystemseng.2007.08.008

Wilding-McBride, D., & Pun, D. (2018). Mask R-CNN utils. GitHub. https://github.com/DiUS/MaskRCNN-utils

Wongsriworaphon, A., Arnonkijpanich, B., & Pathumnakul, S. (2015). An approach based on digital image analysis to estimate the live weights of pigs in farm environments. Computers and Electronics in Agriculture, 115, 26–33. https://doi.org/10.1016/j.compag.2015.05.004 DOI: https://doi.org/10.1016/j.compag.2015.05.004

RCSI

Published

2024-01-10

How to Cite

Ormeño-Ayala, Y. I., & Zapata-Ttito, A. G. (2024). Application of the convolutional network Mask R-CNN for the estimation of the body weight of the guinea pig. Revista Científica De Sistemas E Informática, 4(1), e614. https://doi.org/10.51252/rcsi.v4i1.614