Design and Implementation of The Smart Weighing Precision Livestock Monitoring Technology based on the Internet of Things (IoT)

Tri Kushartadi, Muh. Asnoer Laagu, Muhamad Asvial

Abstract


The traditional approach to weight measurement, which measures each sheep individually, is time-consuming, and sometimes, human error triggers other issues, such as data validation, sheep classification, etc. Most farmers or breeders nowadays still manage their livestock traditionally, which is inefficient. We proposed that the Livestock Live Monitoring System is designed to collect measured data in real-time and display data in graphics; these models combine Bluetooth Low Energy as a wearable sensor to identify animals and Smart Weight Measurement to deliver weight and health data to the cloud system. This system aims to measure livestock and store the data in the server application so livestock monitoring can be done in real-time and remotely. The technology used in this system is ESP32, Load Cell, Bluetooth Low Energy, and Message Queue Telemetry Transport Protocol. Wearable devices act as an identification tag for livestock, and a smart weight scale is used to weigh the livestock and integrate it with the system. Two sheep are used as experiment objects, and their measured weight is compared to their weight when measured traditionally using a conventional scale. Based on the experiment, the weight data measured using the system has an accuracy of 99.82% for sheep number 1 and 99.17% for sheep number 2. This proposed system provides many benefits, including real-time livestock monitoring, cost efficiency, and an efficient feeding system for sheep using weight data.

Keywords


Internet of Things; smart weight scale; Bluetooth low energy identification; weight data; sheep classification.

Full Text:

PDF

References


W. Peng and E. M. Berry, “The concept of food security,” Encycl. Food Secur. Sustain., no. January, pp. 1–7, 2018, doi: 10.1016/B978-0-08-100596-5.22314-7.

A. Salama, A. E. Hassanien, and A. Fahmy, “Sheep Identification Using a Hybrid Deep Learning and Bayesian Optimization Approach,” IEEE Access, vol. 7, pp. 31681–31687, 2019, doi: 10.1109/ACCESS.2019.2902724.

I. S. Center (BPS), “Populasi Domba menurut Provinsi (Ekor),” 2021. .

S. Rahayu, A. Y. Ridwan, and M. Saputra, “Designing Green Warehouse Systems Based on Enterprise Resource Planning for the Leather Tanning Industry,” Proc. Int. Conf. Electr. Eng. Informatics, vol. 2019-July, pp. 602–607, 2019, doi: 10.1109/ICEEI47359.2019.8988819.

R. Sokullu, B. Y. Tanriverdi, and R. Goleva, “Iot Based Livestock Precision Feeding System Using Machine Learning,” 2022 8th Int. Conf. Energy Effic. Agric. Eng. EE AE 2022 - Proc., no. July, pp. 709–712, 2022, doi: 10.1109/EEAE53789.2022.9831244.

M. Asvial, A. Cracias, M. A. Laagu, and A. S. Arifin, “Design and Analysis of Low Power and Lossy Network Routing System for Internet of Things Network,” Int. J. Intell. Eng. Syst., vol. 14, no. 4, pp. 548–560, 2021, doi: 10.22266/ijies2021.0831.48.

J. Song, Q. Zhong, W. Wang, C. Su, Z. Tan, and Y. Liu, “FPDP: Flexible Privacy-Preserving Data Publishing Scheme for Smart Agriculture,” IEEE Sens. J., vol. 21, no. 16, pp. 17430–17438, Aug. 2021, doi: 10.1109/JSEN.2020.3017695.

K. Rb, V. Nayak, J. Singh, and R. Pratap, “Nano-enabled wearable sensors for the Internet of Things ( IoT ),” Mater. Lett., vol. 304, no. August, p. 130614, 2021, doi: 10.1016/j.matlet.2021.130614.

B. I. Akhigbe, K. Munir, O. Akinade, L. Akanbi, and L. O. Oyedele, “IoT Technologies for Livestock Management : A Review of Present Status , Opportunities , and Future Trends,” 2021.

K. Asmoro, I. B. Hidayat, and N. Ibrahim, “Estimation Of Sheep Carcass Weight Based On Geomteric Active Contour Method and Classification of Decision Tree,” vol. 5, no. 3, pp. 4766–4772, 2018.

S. Suwannakhun and P. Daungmala, “Estimating Pig Weight with Digital Image Processing using Deep Learning,” Proc. - 14th Int. Conf. Signal Image Technol. Internet Based Syst. SITIS 2018, no. 5, pp. 320–326, 2018, doi: 10.1109/SITIS.2018.00056.

M. Zhang, X. Wang, H. Feng, Q. Huang, X. Xiao, and X. Zhang, “Wearable Internet of Things enabled precision livestock farming in smart farms : A review of technical solutions for precise perception , biocompatibility , and sustainability monitoring,” J. Clean. Prod., vol. 312, no. February, p. 127712, 2021, doi: 10.1016/j.jclepro.2021.127712.

J. Makario and C. W. Maina, “A bluetooth low energy (BLE) based system for livestock tracking and localization,” 2021 IST-Africa Conf. IST-Africa 2021, pp. 1–7, 2021.

J. Xu, L. Yu, J. Zhang, and Q. Wu, “Automatic Sheep Counting by Multi-object Tracking,” 2020 IEEE Int. Conf. Vis. Commun. Image Process. VCIP 2020, vol. 49, no. December, p. 257, 2020, doi: 10.1109/VCIP49819.2020.9301868.

F. Sarwar, A. Griffin, P. Periasamy, K. Portas, and J. Law, “Detecting and Counting Sheep with a Convolutional Neural Network,” Proc. AVSS 2018 - 2018 15th IEEE Int. Conf. Adv. Video Signal-Based Surveill., pp. 1–6, 2019, doi: 10.1109/AVSS.2018.8639306.

L. Szymanski and M. Lee, “Deep Sheep: Kinship assignment in livestock from facial images,” Int. Conf. Image Vis. Comput. New Zeal., vol. 2020-Novem, 2020, doi: 10.1109/IVCNZ51579.2020.9290558.

I. Ardiansah, N. Bafdal, E. Suryadi, and A. Bono, “Greenhouse monitoring and automation using arduino: A review on precision farming and Internet of Things (IoT),” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 2, pp. 703–709, 2020, doi: 10.18517/ijaseit.10.2.10249.

J. Vaughan, P. M. Green, M. Salter, B. Grieve, and K. B. Ozanyan, “Floor Sensors of Animal Weight and Gait for Precision Livestock Farming,” pp. 9–11, 2017.

S. Jaiteh, S. Farah, A. Suhaimi, T. C. Seong, A. M. Buhari, and H. Neyaz, “Smart Scale Tracking System Using Calibrated Load Cells,” pp. 170–174, 2019.

C. Y. Wang, Y. J. Chen, and C. F. Chien, “Industry 3.5 to empower smart production for poultry farming and an empirical study for broiler live weight prediction,” Comput. Ind. Eng., vol. 151, no. October 2020, p. 106931, 2021, doi: 10.1016/j.cie.2020.106931.

N. Promsuk and A. Taparugssanagorn, “Numerical reader system for digital measurement instruments embedded industrial internet of things,” J. Commun., vol. 16, no. 4, pp. 132–142, 2021, doi: 10.12720/jcm.16.4.132-142.

A. J. Hintaw, S. Manickam, S. Karuppayah, and M. F. Aboalmaaly, “A brief review on MQTT’s security issues within the internet of things (IoT),” J. Commun., vol. 14, no. 6, pp. 463–469, 2019, doi: 10.12720/jcm.14.6.463-469.

A. J. Sairam, T. Reddy Induri, and V. Bagyaveereswaran, “Validation of Wearable Sensors and RFID for Real time Monitoring of Cattle Farming using Internet of Things,” 2019 Innov. Power Adv. Comput. Technol. i-PACT 2019, pp. 4–7, 2019, doi: 10.1109/i-PACT44901.2019.8960050.

N. Azmi et al., “Radio frequency identification (RFID) range test for animal activity monitoring,” 2019 IEEE Int. Conf. Sensors Nanotechnology, SENSORS NANO 2019, pp. 1–4, 2019, doi: 10.1109/SENSORSNANO44414.2019.8940097.

A. Pezzuolo, H. Guo, S. Guercini, and F. Marinello, “Non-contact feed weight estimation by RFID technology in cow-feed alley,” 2020 IEEE Int. Work. Metrol. Agric. For. MetroAgriFor 2020 - Proc., pp. 170–174, 2020, doi: 10.1109/MetroAgriFor50201.2020.9277653.

G. Lee, K. Ogata, K. Kawasue, S. Sakamoto, and S. Ieiri, “Identifying-and-counting based monitoring scheme for pigs by integrating BLE tags and WBLCX antennas,” Comput. Electron. Agric., vol. 198, no. January 2021, p. 107070, 2022, doi: 10.1016/j.compag.2022.107070.

K. Szyc, M. Nikodem, and M. Zdunek, “Bluetooth low energy indoor localization for large industrial areas and limited infrastructure,” Ad Hoc Networks, vol. 139, no. October 2022, p. 103024, 2023, doi: 10.1016/j.adhoc.2022.103024.

O. Breinbjerg and K. Kaslis, “On the Accuracy of Friis Transmission Formula at Short Range,” no. August, pp. 2–3, 2017.

W. S. Jeon, M. H. Dwijaksara, and D. G. Jeong, “Performance analysis of neighbor discovery process in bluetooth low-energy networks,” IEEE Trans. Veh. Technol., vol. 66, no. 2, pp. 1865–1871, 2017, doi: 10.1109/TVT.2016.2558194.

H. Kareem and D. Dunaev, “The Working Principles of ESP32 and Analytical Comparison of using Low-Cost Microcontroller Modules in Embedded Systems Design,” 2021 4th Int. Conf. Circuits, Syst. Simulation, ICCSS 2021, pp. 130–135, 2021, doi: 10.1109/ICCSS51193.2021.9464217.

M. D. D. Ali, S. Hadiyoso, and I. D. Irawati, “Implementation of Low Resource Parking Information System Prototype Based on Wireless Sensor Network,” J. Commun., vol. 17, no. 11, pp. 919–924, 2022, doi: 10.12720/jcm.17.11.919-924.




DOI: http://dx.doi.org/10.18517/ijaseit.13.4.18557

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development