Vision-based measuring method for individual cow feed intake using depth images and a Siamese network
Keywords:
computer vision, Siamese network, cow feed intake, depth image, precision livestock farmingAbstract
Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows, which can also evaluate the utilization rate of pasture feed. To achieve an automatic and non-contact measurement of feed intake, this paper proposes a method for measuring the feed intake of cows based on computer vision technology with a Siamese network and depth images. An automated data acquisition system was first designed to collect depth images of feed piles and constructed a dataset with 24 150 samples. A deep learning model based on the Siamese network was then constructed to implement non-contact measurement of feed intake for dairy cows by training with collected data. The experimental results show that the mean absolute error (MAE) and the root mean square error (RMSE) of this method are 0.100 kg and 0.128 kg in the range of 0-8.2 kg respectively, which outperformed existing works. This work provides a new idea and technology for the intelligent measuring of dairy cow feed intake. Keywords: computer vision, Siamese network, cow feed intake, depth image, precision livestock farming DOI: 10.25165/j.ijabe.20231603.7985 Citation: Wang X J, Dai B S, Wei X L, Shen W Z, Zhang Y G, Xiong B H. Vision-based measuring method for individual cow feed intake using depth images and a Siamese network. Int J Agric & Biol Eng, 2023; 16(3): 233–239.References
[1] Martin Malia J, Dórea J R R, Borchers M R, Wallace R L, Bertics S J, DeNise S K, et al. Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables. Journal of Dairy Science, 2021; 104(8): 8765-8782.
[2] Parsons C T, Dafoe J M, Wyffels S A, Delcurto T, Boss D L. The influence of residual feed intake and cow age on beef cattle performance, supplement intake, resource use, and grazing behavior on winter mixed-grass rangelands. Animals, 2021; 11(6): 1518. doi: 10.3390/ani11061518.
[3] Lawrence P, Kenny D A, Earley B, Mcgee M. Intake of conserved and grazed grass and performance traits in beef suckler cows differing in phenotypic residual feed intake. Livestock Science, 2013; 152(2-3): 154-166.
[4] Wetlesen M S, Åby B A, Vangen O, Aass L. Simulations of feed intake, production output, and economic result within extensive and intensive suckler cow beef production systems. Livestock Science, 2020; 241: 104229. doi: 10.1016/j.livsci.2020.104229.
[5] Smith W B, Galyean M L, Kallenbach R L, Greenwood P L, Scholljegerdes E J. 2021. Board-invited review: understanding intake on pastures: How, why, and a way forward. Journal of Animal Science, 2021; 99(6): skab062. doi: 10.1093/jas/skab062.
[6] Bareille N, Beaudeau F, Billon S, Robert A, Faverdin P. Effects of health disorders on feed intake and milk production in dairy cows. Livestock Production Science, 2003; 83(1): 53-62.
[7] Plaizier J C, Krause D O, Gozho G N, Mcbride B W. Subacute ruminal acidosis in dairy cows: the physiological causes, incidence, and consequences. Veterinary Journal, 2008; 176(1): 21-31.
[8] Halachmi I, Edan Y, Moallem U, Maltz E. Predicting feed intake of the individual dairy cow. Journal of Dairy Science, 2004; 87(7): 2254-2267.
[9] Bach A, Iglesias C, Busto I. Technical note: a computerized system for monitoring feeding behavior and individual feed intake of dairy cattle. Journal of Dairy Science, 2004; 87: 4207–4209.
[10] Chapinal N, Veira D M, Weary D M, von Keyserlingk M A G. Technical note: Validation of a system for monitoring individual feeding and drinking behavior and intake in group-housed cattle. Journal of Dairy Science, 2007; 90(12): 5732–5736.
[11] Chizzotti M L, Machado F S, Valente E, Pereira L, Campos M M, Tomich T R, et al. Technical note: Validation of a system for monitoring individual feeding behavior and individual feed intake in dairy cattle. Journal of Dairy Science, 2015; 98(5): 3438-3442.
[12] Bloch V, Levit H, Halachmi I. Design a system for measuring individual cow feed intake in commercial dairies. Animal, 2021; 15(7): 100277. doi: 10.1016/j.animal.2021.100277.
[13] Oliveira Jr, B R, Ribas M N, Machado F S, Lima J A M Cavalcanti L F L, Chizzotti M L, et al. Validation of a system for monitoring individual feeding and drinking behaviour and intake in young cattle. Animal, 2018; 12(3): 634-639.
[14] Halachmi I, Guarino M, Bewley J, Pastell M. 2018. Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annual Review of Animal Biosciences, 2018; 7: 403-425.
[15] Arcidiacono C, Porto S M C, Mancino M, Cascone G. Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data. Computers and Electronics in Agriculture, 2017; 134: 124-134
[16] Ruuska S, Kajava S, Mughal M, Zehner N, Mononen J. Validation of a pressure sensor-based system for measuring eating, rumination and drinking behaviour of dairy cattle. Applied Animal Behaviour Science, 2016; 174: 19-23.
[17] Norbu N, Alvarez-Hess P S, Leury B J, Wright M M, Douglas M L, Moate P J, et al. Assessment of rumiwatch noseband sensors for the quantification of ingestive behaviors of dairy cows at grazing or fed in stalls. Animal Feed Science and Technology, 2021; 280: 115076. doi: 10.1016/j.anifeedsci.2021.115076.
[18] Galli J R, Cangiano C A, Milone D H, Laca E A. Acoustic monitoring of short-term ingestive behavior and intake in grazing sheep. Livestock Science, 2011; 140(1-3): 32-41.
[19] Shen W Z, Li G, Wei X L, Fu Q, Zhang Y G, Qu T Y, et al. Assessment of dairy cow feed intake based on BP neural network with polynomial decay learning rate. Information Processing in Agriculture, 2022; 9(2): 266-275.
[20] Zhou Y T. Study on the identification of eating behavior of beef cattle and the model of feed intake. Master dissertation. Shenyang: Shenyang Agricultural University, 2018; 58p. (in Chinese)
[21] González L A, Bishop-Hurley G J, Handcock R N, Crossman C. Behavioral classification of data from collars containing motion sensors in grazing cattle. Computers and Electronics in Agriculture, 2015; 110: 91-102.
[22] Shelley A N. Monitoring dairy cow feed intake using machine vision. Master’s dissertation. Lexington: Univerisity of Kentucky, 2013; 99p.
[23] Shelley A N, Lau D L, Stone A E, Bewley J M. Short communication: Measuring feed volume and weight by machine vision. Journal of Dairy Science, 2016; 99: 386-391.
[24] Bloch V, Levit H, Halachmi I. Assessing the potential of photogrammetry to monitor feed intake of dairy cows. Journal of Dairy Research, 2019; 86(1): 1-6.
[25] Bezen R, Edan Y, Halachmi I. Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms. Computers and Electronics in Agriculture, 2020; 172: 105345. doi: 10.1016/j.compag.2020.105345.
[26] Saar M, Edan Y, Godo A, Lepar J, Parmet Y, Halachmi I. A machine vision system to predict individual cow feed intake of different feeds in a cowshed. Animal, 2022; 16(1): 100432. doi: 10.1016/j.animal.2021.100432.
[27] Chopra S, HadSell R, LeCun Y. Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). San Diego: IEEE, 2005; pp.539-546. doi: 10.1109/CVPR.2005.202.
[28] He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas: IEEE, 2016; pp.770-778. doi: 10.1109/CVPR.2016.90.
[2] Parsons C T, Dafoe J M, Wyffels S A, Delcurto T, Boss D L. The influence of residual feed intake and cow age on beef cattle performance, supplement intake, resource use, and grazing behavior on winter mixed-grass rangelands. Animals, 2021; 11(6): 1518. doi: 10.3390/ani11061518.
[3] Lawrence P, Kenny D A, Earley B, Mcgee M. Intake of conserved and grazed grass and performance traits in beef suckler cows differing in phenotypic residual feed intake. Livestock Science, 2013; 152(2-3): 154-166.
[4] Wetlesen M S, Åby B A, Vangen O, Aass L. Simulations of feed intake, production output, and economic result within extensive and intensive suckler cow beef production systems. Livestock Science, 2020; 241: 104229. doi: 10.1016/j.livsci.2020.104229.
[5] Smith W B, Galyean M L, Kallenbach R L, Greenwood P L, Scholljegerdes E J. 2021. Board-invited review: understanding intake on pastures: How, why, and a way forward. Journal of Animal Science, 2021; 99(6): skab062. doi: 10.1093/jas/skab062.
[6] Bareille N, Beaudeau F, Billon S, Robert A, Faverdin P. Effects of health disorders on feed intake and milk production in dairy cows. Livestock Production Science, 2003; 83(1): 53-62.
[7] Plaizier J C, Krause D O, Gozho G N, Mcbride B W. Subacute ruminal acidosis in dairy cows: the physiological causes, incidence, and consequences. Veterinary Journal, 2008; 176(1): 21-31.
[8] Halachmi I, Edan Y, Moallem U, Maltz E. Predicting feed intake of the individual dairy cow. Journal of Dairy Science, 2004; 87(7): 2254-2267.
[9] Bach A, Iglesias C, Busto I. Technical note: a computerized system for monitoring feeding behavior and individual feed intake of dairy cattle. Journal of Dairy Science, 2004; 87: 4207–4209.
[10] Chapinal N, Veira D M, Weary D M, von Keyserlingk M A G. Technical note: Validation of a system for monitoring individual feeding and drinking behavior and intake in group-housed cattle. Journal of Dairy Science, 2007; 90(12): 5732–5736.
[11] Chizzotti M L, Machado F S, Valente E, Pereira L, Campos M M, Tomich T R, et al. Technical note: Validation of a system for monitoring individual feeding behavior and individual feed intake in dairy cattle. Journal of Dairy Science, 2015; 98(5): 3438-3442.
[12] Bloch V, Levit H, Halachmi I. Design a system for measuring individual cow feed intake in commercial dairies. Animal, 2021; 15(7): 100277. doi: 10.1016/j.animal.2021.100277.
[13] Oliveira Jr, B R, Ribas M N, Machado F S, Lima J A M Cavalcanti L F L, Chizzotti M L, et al. Validation of a system for monitoring individual feeding and drinking behaviour and intake in young cattle. Animal, 2018; 12(3): 634-639.
[14] Halachmi I, Guarino M, Bewley J, Pastell M. 2018. Smart animal agriculture: application of real-time sensors to improve animal well-being and production. Annual Review of Animal Biosciences, 2018; 7: 403-425.
[15] Arcidiacono C, Porto S M C, Mancino M, Cascone G. Development of a threshold-based classifier for real-time recognition of cow feeding and standing behavioural activities from accelerometer data. Computers and Electronics in Agriculture, 2017; 134: 124-134
[16] Ruuska S, Kajava S, Mughal M, Zehner N, Mononen J. Validation of a pressure sensor-based system for measuring eating, rumination and drinking behaviour of dairy cattle. Applied Animal Behaviour Science, 2016; 174: 19-23.
[17] Norbu N, Alvarez-Hess P S, Leury B J, Wright M M, Douglas M L, Moate P J, et al. Assessment of rumiwatch noseband sensors for the quantification of ingestive behaviors of dairy cows at grazing or fed in stalls. Animal Feed Science and Technology, 2021; 280: 115076. doi: 10.1016/j.anifeedsci.2021.115076.
[18] Galli J R, Cangiano C A, Milone D H, Laca E A. Acoustic monitoring of short-term ingestive behavior and intake in grazing sheep. Livestock Science, 2011; 140(1-3): 32-41.
[19] Shen W Z, Li G, Wei X L, Fu Q, Zhang Y G, Qu T Y, et al. Assessment of dairy cow feed intake based on BP neural network with polynomial decay learning rate. Information Processing in Agriculture, 2022; 9(2): 266-275.
[20] Zhou Y T. Study on the identification of eating behavior of beef cattle and the model of feed intake. Master dissertation. Shenyang: Shenyang Agricultural University, 2018; 58p. (in Chinese)
[21] González L A, Bishop-Hurley G J, Handcock R N, Crossman C. Behavioral classification of data from collars containing motion sensors in grazing cattle. Computers and Electronics in Agriculture, 2015; 110: 91-102.
[22] Shelley A N. Monitoring dairy cow feed intake using machine vision. Master’s dissertation. Lexington: Univerisity of Kentucky, 2013; 99p.
[23] Shelley A N, Lau D L, Stone A E, Bewley J M. Short communication: Measuring feed volume and weight by machine vision. Journal of Dairy Science, 2016; 99: 386-391.
[24] Bloch V, Levit H, Halachmi I. Assessing the potential of photogrammetry to monitor feed intake of dairy cows. Journal of Dairy Research, 2019; 86(1): 1-6.
[25] Bezen R, Edan Y, Halachmi I. Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms. Computers and Electronics in Agriculture, 2020; 172: 105345. doi: 10.1016/j.compag.2020.105345.
[26] Saar M, Edan Y, Godo A, Lepar J, Parmet Y, Halachmi I. A machine vision system to predict individual cow feed intake of different feeds in a cowshed. Animal, 2022; 16(1): 100432. doi: 10.1016/j.animal.2021.100432.
[27] Chopra S, HadSell R, LeCun Y. Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). San Diego: IEEE, 2005; pp.539-546. doi: 10.1109/CVPR.2005.202.
[28] He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas: IEEE, 2016; pp.770-778. doi: 10.1109/CVPR.2016.90.
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Published
2023-08-17
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Wang, X., Dai, B., Wei, X., Shen, W., Zhang, Y., & Xiong, B. (2023). Vision-based measuring method for individual cow feed intake using depth images and a Siamese network. International Journal of Agricultural and Biological Engineering, 16(3), 233–239. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/7985
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Information Technology, Sensors and Control Systems
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