Animal Reproduction (AR)
https://www.animal-reproduction.org/article/doi/10.1590/1984-3143-AR2023-0077
Animal Reproduction (AR)
Thematic Section: 36th Annual Meeting of the Brazilian Embryo Technology Society (SBTE)

Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview

Paula de Freitas Curti; Alana Selli; Diógenes Lodi Pinto; Alexandre Merlos-Ruiz; Julio Cesar de Carvalho Balieiro; Ricardo Vieira Ventura

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Abstract

Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.

Keywords

machine learning, computer vision, precision livestock farming, sensors

References

Abdul Jabbar K, Hansen MF, Smith ML, Smith LN. Early and non-intrusive lameness detection in dairy cows using 3-dimensional video. Biosyst Eng. 2017;153:63-9.

Akhigbe BI, Munir K, Akinade O, Akanbi L, Oyedele LO. IoT technologies for livestock management: a review of present status, opportunities, and future trends. BDCC. 2021;5(1):10.

Alexandratos N, Bruinsma J. World agriculture towards 2030/2050: the 2012 revision. Roma: FAO; 2012. (ESA Working Paper No. 12-03)

Aungier SPM, Roche JF, Duffy P, Scully S, Crowe MA. The relationship between activity clusters detected by an automatic activity monitor and endocrine changes during the periestrous period in lactating dairy cows. J Dairy Sci. 2015;98(3):1666-84. PMid:25529424.

Ballard DH, Brown CM. Computer vision. Englewood Cliffs, NJ: Prentice-Hall; 1982.

Barreto ADN, Barioni W Jr, Pezzopane JRM, Bernardi ACDC, Pedroso ADF, Marcondes CR, et al. Thermal comfort and behavior of beef cattle in pasture-based systems monitored by visual observation and electronic device. Appl Anim Behav Sci. 2022;253:105687.

Berntsen J, Rimestad J, Lassen JT, Tran D, Kragh MF. Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences. PLoS One. 2022;17(2):e0262661. http://dx.doi.org/10.1371/journal.pone.0262661. PMid:35108306.

Berry DP. Large-scale phenotyping and genotyping: state of the art and emerging challenges. In: Kyriazakis I, editor. Smart livestock nutrition. Belfast: Springer; 2023. p. 103-39. http://dx.doi.org/10.1007/978-3-031-22584-0_5.

Bezen R, Edan Y, Halachmi I. Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms. Comput Electron Agric. 2020;172:105345.

Bloch V, Frondelius L, Arcidiacono C, Mancino M, Pastell M. Development and analysis of a CNN- and transfer-learning-based classification model for automated dairy cow feeding behavior recognition from accelerometer data. Sensors (Basel). 2023;23(5):2611. PMid:36904813.

Bowler AL, Bakalis S, Watson NJ. A review of in-line and on-line measurement techniques to monitor industrial mixing processes. Chem Eng Res Des. 2020;153:463-95.

Brossard L, van Milgen J, Dourmad J-Y, Gaillard C. Smart pig nutrition in the Digital Era. In: Kyriazakis I, editor. Smart livestock nutrition. Belfast: Springer; 2023. p. 169-99. http://dx.doi.org/10.1007/978-3-031-22584-0_7.

Buurman B, Kamruzzaman J, Karmakar G, Islam S. Low-power wide-area networks: design goals, architecture, suitability to use cases and research challenges. IEEE Access. 2020;8:17179-220.

Cabrera VE, Barrientos-Blanco JA, Delgado H, Fadul-Pacheco L. Symposium review: real-time continuous decision making using big data on dairy farms. J Dairy Sci. 2020;103(4):3856-66. PMid:31864744.

Castro W, Marcato J Jr, Polidoro C, Osco LP, Gonçalves W, Rodrigues L, et al. Deep learning applied to phenotyping of biomass in forages with UAV-based RGB imagery. Sensors (Basel). 2020;20(17):4802. PMid:32858803.

Cockburn M. Review: Application and prospective discussion of machine learning for the management of dairy farms. Animals (Basel). 2020;10(9):1690. PMid:32962078.

Firk R, Stamer E, Junge W, Krieter J. Automation of oestrus detection in dairy cows: a review. Livest Prod Sci. 2002;75(3):219-32.

Foley É, Guillemette MG. What is business intelligence? Int J Bus Intell Res. 2010;1(4):1-28.

Føre M, Alver MO, Frank K, Alfredsen JA. Advanced Technology in Aquaculture - Smart Feeding in Marine Fish Farms. In: Kyriazakis I, editor. Smart livestock nutrition. Belfast: Springer; 2023. p. 227-68. http://dx.doi.org/10.1007/978-3-031-22584-0_9.

Ghaffari MH, Monneret A, Hammon HM, Post C, Müller U, Frieten D, Gerbert C, Dusel G, Koch C. Deep convolutional neural networks for the detection of diarrhea and respiratory disease in preweaning dairy calves using data from automated milk feeders. J Dairy Sci. 2022;105(12):9882-95. PMid:36307243.

Giro A, Bernardi ACDC, Barioni W Jr, Lemes AP, Botta D, Romanello N, et al. Application of microchip and infrared thermography for monitoring body temperature of beef cattle kept on pasture. J Therm Biol. 2019;84:121-8. PMid:31466744.

Göncü S, Koluman N. The sensor technologies for more efficient cow reproduction systems. MOJES. 2019;4(3):128-31. http://dx.doi.org/10.15406/mojes.2019.04.00144.

Gong C, Zhang Y, Wei Y, Du X, Su L, Weng Z. Multicow pose estimation based on keypoint extraction. PLoS One. 2022;17(6):e0269259. http://dx.doi.org/10.1371/journal.pone.0269259. PMid:35657811.

González LA, Imaz JA, Chang-Fung-Martel J. Smart nutrition of extensively kept ruminants. In: Kyriazakis I, editor. Smart livestock nutrition. Belfast: Springer; 2023. p. 269-309. http://dx.doi.org/10.1007/978-3-031-22584-0_10.

Gray J, Banhazi TM, Kist AA. Wireless data management system for environmental monitoring in livestock buildings. Inf Process Agric. 2017;4(1):1-17.

Halachmi I, Guarino M, Bewley J, Pastell M. Smart Animal Agriculture: application of real-time sensors to improve animal well-being and production. Annu Rev Anim Biosci. 2019;7(1):403-25. PMid:30485756.

Hidayatullah P, Mengko TLER, Munir R, Barlian A. Bull sperm tracking and machine learning-based motility classification. IEEE Access. 2021;9:61159-70.

Huang X, Hu Z, Wang X, Yang X, Zhang J, Shi D. An improved single shot multibox detector method applied in body condition score for dairy cows. Animals (Basel). 2019;9(7):470. PMid:31340515.

Jegan G, Sheeba IR, Priya PK, Joany RM, Vino T. Cattle tracking system architecture using LORA. In: 2022 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS) [Internet]. Chennai, India: IEEE; 2022 [cited 2023 May 16]. p. 1-4. Available from: https://ieeexplore.ieee.org/document/10046673/

Keller A, Kerns K. Deep learning, artificial intelligence methods to predict boar sperm acrosome health. Anim Reprod Sci. 2022;247:107110.

Klaina H, Guembe IP, Lopez-Iturri P, Campo-Bescós MÁ, Azpilicueta L, Aghzout O, et al. Analysis of low power wide area network wireless technologies in smart agriculture for large-scale farm monitoring and tractor communications. Measurement. 2022;187:110231.

Kliemann RD, Fernandes SR, Campos MM, Tomich TR, Pereira LGR, Neto AFG. Sensor validation to record rumination and analysis of behavioral parameters of dairy Gyr heifers in feedlot systems. Trop Anim Health Prod. 2023;55(3):166. PMid:37083857.

Kunc P, Knízková I, Prikryl M, Maloun J. Infrared thermography as a tool to study the milking process: a review article. Agric Trop Subtrop [Internet]. 2007 [cited 2023 May 16];40(1):29-32. Available from: https://www.researchgate.net/publication/284125983

Kyriazakis I. Putting smart into nutrition. In: Kyriazakis I, editor. Smart livestock nutrition. Belfast: Springer; 2023. p. 1-15. http://dx.doi.org/10.1007/978-3-031-22584-0_1.

Leighton PLA, Segura J, Lam S, Marcoux M, Wei X, Lopez-Campos O, Soladoye P, Dugan MER, Juarez M, Prieto N. Prediction of carcass composition and meat and fat quality using sensing technologies: a review. Meat and Muscle Biology. 2022;5(3):1-21. http://dx.doi.org/10.22175/mmb.12951.

Li X, Cai C, Zhang R, Ju L, He J. Deep cascaded convolutional models for cattle pose estimation. Comput Electron Agric. 2019;164:104885.

Li Y, Wu FX, Ngom A. A review on machine learning principles for multi-view biological data integration. Brief Bioinform. 2018;19(2):325-40. http://dx.doi.org/10.1093/bib/bbw113. PMid:28011753.

Lima JS, Ribeiro DCSZ, Neto HA, Campos SVA, Leite MO, Fortini MEDR, de Carvalho BPM, Almeida MVO, Fonseca LM. A machine learning proposal method to detect milk tainted with cheese whey. J Dairy Sci. 2022;105(12):9496-508. PMid:36207182.

Liseune A, Salamone M, Van den Poel D, van Ranst B, Hostens M. Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning. Comput Electron Agric. 2021;180:105904.

Madureira AML, Silper BF, Burnett TA, Polsky L, Cruppe LH, Veira DM, Vasconcelos JL, Cerri RL. Factors affecting expression of estrus measured by activity monitors and conception risk of lactating dairy cows. J Dairy Sci. 2015;98(10):7003-14. PMid:26254517.

Matos FD, Rocha JC, Nogueira MFG. A method using artificial neural networks to morphologically assess mouse blastocyst quality. J Anim Sci Technol. 2014;56(1):15. PMid:26290704.

Mishra S, Sharma SK. Advanced contribution of IoT in agricultural production for the development of smart livestock environments. Internet of Things (Netherlands). 2023;22:100724. http://dx.doi.org/10.1016/j.iot.2023.100724.

Morrone S, Dimauro C, Gambella F, Cappai MG. Industry 4.0 and Precision Livestock Farming (PLF): an up to date overview across animal productions. Sensors (Basel). 2022;22(12):4319. PMid:35746102.

Mottram T. Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection. Animal. 2016;10(10):1575-84. PMid:26608699.

Neethirajan S. The role of sensors, big data and machine learning in modern animal farming. Sens Biosensing Res. 2020;29:100367. http://dx.doi.org/10.1016/j.sbsr.2020.100367.

Nejati A, Bradtmueller A, Shepley E, Vasseur E. Technology applications in bovine gait analysis: a scoping review. PLoS One. 2023;18(1):e0266287. http://dx.doi.org/10.1371/journal.pone.0266287. PMid:36696371.

Noe SM, Zin TT, Tin P, Kobayashi I. Automatic detection and tracking of mounting behavior in cattle using a deep learning-based instance segmentation model. Int J Innov Comput, Inf Control. 2022;18(1):211-20.

Norton T, Chen C, Larsen MLV, Berckmans D. Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer. Animal. 2019;13(12):3009-17. PMid:31516101.

Oliveira DAB, Pereira LGR, Bresolin T, Ferreira REP, Dorea JRR. A review of deep learning algorithms for computer vision systems in livestock. Livest Sci. 2021;253:104700.

Pasolini G. On the LoRa chirp spread spectrum modulation: signal properties and their impact on transmitter and receiver architectures. IEEE Trans Wirel Commun. 2022;21(1):357-69.

Pinto DL, Selli A, Tulpan D, Andrietta LT, Garbossa PLM, Voort GV, Munro J, McMorris M, Alves AAC, Carvalheiro R, Poleti MD, Balieiro JCC, Ventura RV. Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms. Livest Sci. 2023;267:105512. http://dx.doi.org/10.1016/j.livsci.2022.105152.

Qiao Y, Kong H, Clark C, Lomax S, Su D, Eiffert S, et al. Intelligent perception for cattle monitoring: a review for cattle identification, body condition score evaluation, and weight estimation. Comput Electron Agric. 2021;185:106143.

Rabaglino MB, Salilew‐Wondim D, Zolini A, Tesfaye D, Hoelker M, Lonergan P, Hansen PJ. Machine‐learning methods applied to integrated transcriptomic data from bovine blastocysts and elongating conceptuses to identify genes predictive of embryonic competence. FASEB J. 2023;37(3):e22809. PMid:36753406.

Rocha JC, Passalia FJ, Matos FD, Takahashi MB, Ciniciato DDS, Maserati MP, Alves MF, Almeida TG, Cardoso BL, Basso AC, Nogueira MFG. A method based on artificial intelligence to fully automatize the evaluation of bovine blastocyst images. Sci Rep. 2017a;7(1):7659. PMid:28794478.

Rocha JC, Passalia FJ, Matos FD, Takahashi MB, Maserati MP Jr, Alves MF, de Almeida TG, Cardoso BL, Basso AC, Nogueira MFG. Automatized image processing of bovine blastocysts produced in vitro for quantitative variable determination. Sci Data. 2017b;4(1):170192. PMid:29257125.

Rorie RW, Bilby TR, Lester TD. Application of electronic estrus detection technologies to reproductive management of cattle. Theriogenology. 2002;57(1):137-48. PMid:11775966.

Rosales-Pérez A. A review on machine learning techniques for acute leukemia classification. In: Torres Garcia AA, editor. Biosignal processing and classification using computational learning and intelligence. Amsterdam: Academic Press; 2022. p. 429-46. http://dx.doi.org/10.1016/B978-0-12-820125-1.00033-6.

Rutten CJ, Velthuis AGJ, Steeneveld W, Hogeveen H. Invited review: sensors to support health management on dairy farms. J Dairy Sci. 2013;96(4):1928-52. PMid:23462176.

Sampaio VSOL, De Macedo DDJ, Britto A. Video7: An Architecture for Storage and Recovery of Streaming Audio and Video in NoSQL Database. In: 2018 Symposium on High Performance Computing Systems (WSCAD). São Paulo, Brazil: IEEE; 2018. p. 45-52. http://dx.doi.org/10.1109/WSCAD.2018.00017.

Sarker IH. AI-based modeling: techniques, applications and research issues towards automation, intelligent and smart systems. SN COMPUT SCI. 2022;3(2):158. PMid:35194580.

Sasloglou KA, Glover I, Goh HG, Kwong KH, Gilroy MP, Tachtatzis C, et al. Antenna and Base-Station Diversity for WSN Livestock Monitoring. WSN. 2009;01(5):383-96.

Schori F, Münger A. Assessment of two wireless reticulo-rumen pH sensors for dairy cows. 2022 [cited 2023 Jun 20]. Available from: https://ira.agroscope.ch/de-CH/publication/48801

Selli A, Ventura RV, Fonseca PAS, Buzanskas ME, Andrietta LT, Balieiro JCC, Brito LF. Detection and visualization of heterozygosity-rich regions and runs of homozygosity in worldwide sheep populations. Animals (Basel). 2021;11(9):2696. PMid:34573664.

Seminger D, terHeerdt J, Brenner L, Duncan O, Sparkman M, Sherer T, et al. Data Types in power BI Desktop - Power BI [Internet]. 2023 [cited 2023 May 12]. Available from: https://learn.microsoft.com/en-us/power-bi/connect-data/desktop-data-types

Slob N, Catal C, Kassahun A. Application of machine learning to improve dairy farm management: a systematic literature review. Prev Vet Med. 2021;187:105237. PMid:33418514.

Souza AM, Weigert RDAS, Machado De Sousa EP, Tassoni Andrietta L, Ventura RV. Practical implications of using non‐relational databases to store large genomic data files and novel phenotypes. J Anim Breed Genet. 2022;139(1):100-12. PMid:34459042.

Tullo E, Finzi A, Guarino M. Review: environmental impact of livestock farming and Precision Livestock Farming as a mitigation strategy. Sci Total Environ. 2019;650(Pt 2):2751-60. PMid:30373053.

Wang Y, Kang X, He Z, Feng Y, Liu G. Accurate detection of dairy cow mastitis with deep learning technology: a new and comprehensive detection method based on infrared thermal images. Animal. 2022;16(10):100646. http://dx.doi.org/10.1016/j.animal.2022.100646. PMid:36183435.

Watanabe RN, Bernardes PA, Romanzini EP, Braga LG, Brito TR, Teobaldo RW, Reis RA, Munari DP. Strategy to predict high and low frequency behaviors using triaxial accelerometers in grazing of beef cattle. Animals (Basel). 2021;11(12):3438. PMid:34944215.

Wu D, Han M, Song H, Song L, Duan Y. Monitoring the respiratory behavior of multiple cows based on computer vision and deep learning. J Dairy Sci. 2023;106(4):2963-79. PMid:36797189.

Wu D, Yin X, Jiang B, Jiang M, Li Z, Song H. Detection of the respiratory rate of standing cows by combining the Deeplab V3+ semantic segmentation model with the phase-based video magnification algorithm. Biosyst Eng. 2020;192:72-89.

Zhang L, Kim J, Lee Y. The platform development of a real-time momentum data collection system for livestock in wide grazing land. Electronics (Basel). 2018;7(5):71.

Zhao K, Bewley JM, He D, Jin X. Automatic lameness detection in dairy cattle based on leg swing analysis with an image processing technique. Comput Electron Agric. 2018;148:226-36.

Zuidhof MJ, Afrouziyeh M, Chang-Fung-Martel J, You J, van der Klein SAS. Smart poultry nutrition. In: Kyriazakis I, editor. Smart livestock nutrition. Belfast: Springer; 2023. p. 201-25. http://dx.doi.org/10.1007/978-3-031-22584-0_8.
 


Submitted date:
05/22/2023

Accepted date:
07/10/2023

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