Header Image Title: CLOUD DATAFLOW FOR MACHINE LEARNING MODELING ON IOT DATA IN SMART LIVESTOCK FARMING
Peer-reviewed articles 17,970 +
ARTICLE METRICS


Altmetrics info

Title: CLOUD DATAFLOW FOR MACHINE LEARNING MODELING ON IOT DATA IN SMART LIVESTOCK FARMING
CLOUD DATAFLOW FOR MACHINE LEARNING MODELING ON IOT DATA IN SMART LIVESTOCK FARMING
Kristina Dineva; Tatiana Atanasova; Todor Balabanov
10.5593/sgem2022/6.1/s25.09
10.5593/sgem2022/6.1
1314-2704
978-619-7603-48-4
English
22
6.1
•    Prof. DSc. Oleksandr Trofymchuk, UKRAINE 
•    Prof. Dr. hab. oec. Baiba Rivza, LATVIA
Advances in Biotechnology
The development of modern dairy farming is aimed at creating larger farms with more intensive production in accordance with the market needs. New larger livestock facilities need new approaches to management and logistics. The Internet of Things (IoT), machine learning (ML) and cloud computing are forming the basis of digital animal husbandry as they are increasingly being introduced into dairy farming. The collection of IoT sensor data and their storage in the information cloud allows the use of machine learning methods for predicting events in livestock farms. ML is characterized by its hunger for computing resources in all its phases, which can be resolved using cloud computing. Collected data by IoT devices require cleaning and scaling. Building a model requires training, testing, and validation. All these activities should be carried out in a timely sequence.
The purpose of this article is to build models trained to predict the future amount of milk with the greatest accuracy for each individual animal. To achieve this goal, a pure Azure Cloud DataFlow (ADF) has been created, which monitors the processes from collecting and storing IoТ data, to data processing, modelling, and model evaluation to visualization of results. Following this data flow, the experimental studies described in this article are performed. Three regression machine learning models were trained on the data collected from a Smart Livestock farm. Testing of the developed models has proven the applicability of the developed Cloud DataFlow, as the Boosted Decision Tree Regression Model shows the highest accuracy in predicting the amount of milk produced by each individual animal.
Internet of Things (IoT), Machine Learning (ML), Smart Livestock Monitoring, Azure Cloud DataFlow (ADF)
[1] Neethirajan S., Kemp B., Digital Livestock Farming, Sensing and Bio-Sensing Research, vol. 32, 100408, 2021.
[2] Dineva K., Atanasova T., Expandable IoT Architecture for Livestock in Agriculture 5.0. Proc. 21st Int. Multidisciplinary Sci. Geoconf. SGEM 2021, pp. 147-154, 2021.
[3] Park J. K. and Park E. Y., Animal Monitoring Scheme in Smart Farm using CloudBased System, ECTI-CIT Transactions, vol. 15, no. 1, pp. 24–33, Nov. 2020.
[4] Vigneswari T., Kalaiselvi N., Mathumitha K., Niveditha A., Sowmian A. Smart IOT Cloud Based Livestock Monitoring System: A Survey, TURCOMAT, vol. 12, 2021.
[5] Eastwood, C., Ayre, M., Nettle, R., Dela Rue, B., Making sense in the cloud: Farm advisory services in a smart farming future, Wageningen J. Life Sci., vol.12, no.1, 2019.
[6] Durai S.K.S., Shamili M. D., Smart farming using Machine Learning and Deep Learning techniques, Decision Analytics Journal, vol. 3, 100041, 2022.
[7] Venkatesan, S., Lim, J., Ko, H., Cho, Y., A Machine Learning Based Model for Energy Usage Peak Prediction in Smart Farms. Electronics, vol. 11, 218, 2022.
[8] Gurrapu, S., Sikder, N., Wang, P., Gorentala, N., Williams, M., Batarseh, F. A., Applications of Machine Learning for Precision Agriculture and Smart Farming. The Int .l FLAIRS Conf. Proc., 34. 2021.
[9] Sharma A., Jain A., Gupta P., Chowdary V., Machine Learning Applications for Precision Agriculture: A Comprehensive Review, IEEE Access, vol. 9, 2021.
[10] Kalyani, Y.; Collier, R. A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture. Sensors, vol. 21, 5922, 2021.
[11] Tsochev G., Trifonov R., Cloud computing security requirements: A Review, IOP Conf. Ser.: Mater. Sci. Eng., vol.1216, 012001, 2022.
This research is partly supported by the Ministry of Education and Science of the Republic Bulgaria under the National Science Program INTELLIGENT ANIMAL HUSBANDRY, grant agreement No. Д01-62/18.03.2021/.
conference
https://doi.org/10.5593/sgem2022/6.1/s25.09
Download PDF
Proceedings of 22nd International Multidisciplinary Scientific GeoConference SGEM 2022
22nd International Multidisciplinary Scientific GeoConference SGEM 2022, 04 - 10 July, 2022
Proceedings Paper
STEF92 Technology
International Multidisciplinary Scientific GeoConference SGEM
SWS Scholarly Society; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Serbian Acad Sci and Arts; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; European Acad Sci, Arts and Letters; Acad Fine Arts Zagreb Croatia; Croatian Acad Sci and Arts; Acad Sci Moldova; Montenegrin Acad Sci and Arts; Georgian Acad Sci; Acad Fine Arts and Design Bratislava; Turkish Acad Sci.
73-80
04 - 10 July, 2022
website
8637