Developing a Digital Scales System using Internet of Things Technology on Indonesia Digital Farm
(1) Master of Informatics, Universitas Amikom Yogyakarta, Indonesia
(2) Department of Computer Engineering, Universitas Amikom Yogyakarta, Indonesia
(3) Master of Informatics, Universitas Amikom Yogyakarta, Indonesia
(4) Department of Computer Engineering, Universitas Amikom Yogyakarta, Indonesia
(5) Department of Computer Engineering, Universitas Amikom Yogyakarta, Indonesia
(6) Master of Informatics, Universitas Amikom Yogyakarta, Indonesia
(7) Master of Informatics, Universitas Amikom Yogyakarta, Indonesia
Abstract
Purpose: This research aims to develop a digital scales system using internet of things technology on Indonesia digital farm.
Methods: The stages of the research were carried out starting from literature studies, system requirements analysis, digital scales system design, system testing, and analysis of system test results. This model consists of hardware and software. The hardware consists of sensors for data collection in the field or using cameras, data input devices, data senders to data centers, data centers, and data processors, and data output that can be accessed on a laptop or a smartphone in real-time.
Result: The results of the study show that IoT-based digital scales can be used to read goat weighing results based on RFID data input and camera image capture. The average body weight of a goat that has been weighed is 106.5 pounds, while the average body height of a goat is 150.7 cm.
Novelty: The IoT-based digital scales system (IoT-DSS) can be used to measure the weight and height of goats so that the weighing process is more efficient.
Keywords
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L. Shang, T. Heckelei, M. K. Gerullis, J. Börner, and S. Rasch, “Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction,” Agric. Syst., vol. 190, 2021, doi: 10.1016/j.agsy.2021.103074.
H. Park, S. Y. Choi, H. S. Kang, and N. ji Kwon, “Multi residue determination of 96 veterinary drug residues in domestic livestock and fishery products in South Korea,” Aquaculture, vol. 553, no. February, p. 738064, 2022, doi: 10.1016/j.aquaculture.2022.738064.
T. Norton, C. Chen, M. L. V. Larsen, and D. Berckmans, “Review: Precision livestock farming: Building ‘digital representations’ to bring the animals closer to the farmer,” Animal, vol. 13, no. 12, pp. 3009–3017, 2019, doi: 10.1017/S175173111900199X.
S. SHIBUSAWA, “Digital Farming Approach Changes the Context,” IFAC-PapersOnLine, vol. 51, no. 17, pp. 67–69, 2018, doi: 10.1016/j.ifacol.2018.08.062.
C. Aquilani, A. Confessore, R. Bozzi, F. Sirtori, and C. Pugliese, “Review: Precision Livestock Farming technologies in pasture-based livestock systems,” Animal, vol. 16, no. 1, p. 100429, 2022, doi: 10.1016/j.animal.2021.100429.
T. Groher, K. Heitkämper, and C. Umstätter, “Digital technology adoption in livestock production with a special focus on ruminant farming,” Animal, vol. 14, no. 11, pp. 2404–2413, 2020, doi: 10.1017/S1751731120001391.
S. Neethirajan and B. Kemp, “Digital Livestock Farming,” Sens. Bio-Sensing Res., vol. 32, no. December 2020, p. 100408, 2021, doi: 10.1016/j.sbsr.2021.100408.
P. Niloofar et al., “Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges,” Comput. Electron. Agric., vol. 190, no. September, p. 106406, 2021, doi: 10.1016/j.compag.2021.106406.
L. S. Handigolkar, M. L. Kavya, and P. D. Veena, “Iot Based Smart Poultry Farming using Commodity Hardware and Software,” Bonfring Int. J. Softw. Eng. Soft Comput., vol. 6, no. Special Issue, pp. 171–175, 2016, doi: 10.9756/bijsesc.8269.
A. Prakash, V. K. Saxena, and M. K. Singh, “Genetic analysis of residual feed intake, feed conversion ratio and related growth parameters in broiler chicken: a review,” Worlds. Poult. Sci. J., vol. 76, no. 2, pp. 304–317, 2020, doi: 10.1080/00439339.2020.1735978.
N. Roux et al., “Embodied HANPP of feed and animal products: Tracing pressure on ecosystems along trilateral livestock supply chains 1986–2013,” Sci. Total Environ., vol. 851, no. December 2021, p. 158198, 2022, doi: 10.1016/j.scitotenv.2022.158198.
S. Ingrand, “Opinion paper: ‘Monitoring te salutant:’ combining digital sciences and agro-ecology to design multi-performant livestock farming systems,” Animal, vol. 12, no. 1, pp. 2–3, 2018, doi: 10.1017/S1751731117001999.
P. P. Ray, “A survey on Internet of Things architectures,” J. King Saud Univ. - Comput. Inf. Sci., vol. 30, no. 3, pp. 291–319, 2018, doi: 10.1016/j.jksuci.2016.10.003.
Y. Bai et al., “Instability of decoupling livestock greenhouse gas emissions from economic growth in livestock products in the Tibetan highland,” J. Environ. Manage., vol. 287, no. December 2020, p. 112334, 2021, doi: 10.1016/j.jenvman.2021.112334.
H. C. Lee, C. M. Chen, J. T. Wei, and H. Y. Chiu, “Analysis of veterinary drug residue monitoring results for commercial livestock products in Taiwan between 2011 and 2015,” J. Food Drug Anal., vol. 26, no. 2, pp. 565–571, 2018, doi: 10.1016/j.jfda.2017.06.008.
S. K. Awasthi et al., “Multi-criteria research lines on livestock manure biorefinery development towards a circular economy: From the perspective of a life cycle assessment and business models strategies,” J. Clean. Prod., vol. 341, no. January, p. 130862, 2022, doi: 10.1016/j.jclepro.2022.130862.
L. F. Si, M. Y. Li, and L. He, “Farmland monitoring and livestock management based on internet of things,” Internet of Things (Netherlands), vol. 19, no. July, p. 100581, 2022, doi: 10.1016/j.iot.2022.100581.
F. N. Fote, A. Roukh, S. Mahmoudi, S. A. Mahmoudi, and O. Debauche, “Toward a big data knowledge-base management system for precision livestock farming,” Procedia Comput. Sci., vol. 177, pp. 136–142, 2020, doi: 10.1016/j.procs.2020.10.021.
S. Neethirajan and B. Kemp, “Digital Livestock Farming,” Sens. Bio-Sensing Res., vol. 32, no.
December 2020, p. 100408, 2021, doi: 10.1016/j.sbsr.2021.100408.
N. Kurniawan, “Electrical Energy Monitoring System and Automatic Transfer Switch (ATS)
Controller with the Internet of Things for Solar Power Plants,” J. Soft Comput. Explor., vol. 1, no.
, Sep. 2020, doi: 10.52465/joscex.v1i1.2.
P. Niloofar et al., “Data-driven decision support in livestock farming for improved animal health,
welfare and greenhouse gas emissions: Overview and challenges,” Comput. Electron. Agric., vol.
, no. September, p. 106406, 2021, doi: 10.1016/j.compag.2021.106406.
L. S. Handigolkar, M. L. Kavya, and P. D. Veena, “Iot Based Smart Poultry Farming using
Commodity Hardware and Software,” Bonfring Int. J. Softw. Eng. Soft Comput., vol. 6, no. Special
Issue, pp. 171–175, 2016, doi: 10.9756/bijsesc.8269.
A. Prakash, V. K. Saxena, and M. K. Singh, “Genetic analysis of residual feed intake, feed
conversion ratio and related growth parameters in broiler chicken: a review,” Worlds. Poult. Sci.
J., vol. 76, no. 2, pp. 304–317, 2020, doi: 10.1080/00439339.2020.1735978.
N. Roux et al., “Embodied HANPP of feed and animal products: Tracing pressure on ecosystems
along trilateral livestock supply chains 1986–2013,” Sci. Total Environ., vol. 851, no. December
, p. 158198, 2022, doi: 10.1016/j.scitotenv.2022.158198.
S. Ingrand, “Opinion paper: ‘Monitoring te salutant:’ combining digital sciences and agro-ecology
to design multi-performant livestock farming systems,” Animal, vol. 12, no. 1, pp. 2–3, 2018, doi:
1017/S1751731117001999.
P. P. Ray, “A survey on Internet of Things architectures,” J. King Saud Univ. - Comput. Inf. Sci.,
vol. 30, no. 3, pp. 291–319, 2018, doi: 10.1016/j.jksuci.2016.10.003.
H. C. Lee, C. M. Chen, J. T. Wei, and H. Y. Chiu, “Analysis of veterinary drug residue monitoring
results for commercial livestock products in Taiwan between 2011 and 2015,” J. Food Drug Anal.,
vol. 26, no. 2, pp. 565–571, 2018, doi: 10.1016/j.jfda.2017.06.008.
S. K. Awasthi et al., “Multi-criteria research lines on livestock manure biorefinery development
towards a circular economy: From the perspective of a life cycle assessment and business models
strategies,” J. Clean. Prod., vol. 341, no. January, p. 130862, 2022, doi:
1016/j.jclepro.2022.130862.
L. F. Si, M. Y. Li, and L. He, “Farmland monitoring and livestock management based on internet
of things,” Internet of Things (Netherlands), vol. 19, no. July, p. 100581, 2022, doi:
1016/j.iot.2022.100581.
W. Iwasaki, N. Morita, and M. P. B. Nagata, IoT sensors for smart livestock management. Elsevier
Inc., 2019. doi: 10.1016/B978-0-12-815409-0.00015-2.
F. N. Fote, A. Roukh, S. Mahmoudi, S. A. Mahmoudi, and O. Debauche, “Toward a big data
knowledge-base management system for precision livestock farming,” Procedia Comput. Sci., vol.
, pp. 136–142, 2020, doi: 10.1016/j.procs.2020.10.021.
M. W. Sari, B. Santoso, and M. N. A. Azman, “Implementing Geo Positioning System for Children
Tracking Location Monitoring based on Android,” Sci. J. Informatics, vol. 8, no. 1, pp. 161–167,
, doi: 10.15294/sji.v8i1.27436.
M. W. Sari, Herianto, I. G. B. B. Dharma, and A. E. Tontowi, “Design of Product Monitoring
System Using Internet of Things Technology for Smart Manufacturing,” IOP Conf. Ser. Mater. Sci.
Eng., vol. 835, pp. 1–7, 2020, doi: 10.1088/1757-899X/835/1/012048.
R. Muhendra and A. Amin, “Real-Time Monitoring: Development of Low Power Fire Detection
System for Dense Residential Housing Based on Internet of Things (IoT) and Cloud Messenger,”
Sci. J. Informatics, vol. 8, no. 2, pp. 202–212, 2021, doi: 10.15294/sji.v8i2.30811.
C. Casenave, A. Bisson, S. Boudsocq, and T. Daufresne, “Impact of biological nitrogen fixation
and livestock management on the manure transfer from grazing land in mixed farming systems,” J.
Theor. Biol., vol. 545, p. 111136, 2022, doi: 10.1016/j.jtbi.2022.111136.
F. Barragán, J. Labastida, and A. Ramírez-Hernández, “Response of dung beetle diversity to three
livestock management systems in drylands of central Mexico,” J. Arid Environ., vol. 193, no. July,
, doi: 10.1016/j.jaridenv.2021.104598.
Q. Yue, P. Guo, H. Wu, Y. Wang, and C. Zhang, “Towards sustainable circular agriculture: An
integrated optimization framework for crop-livestock-biogas-crop recycling system management
under uncertainty,” Agric. Syst., vol. 196, no. November 2021, p. 103347, 2022, doi:
1016/j.agsy.2021.103347.
M. S. Gaballah et al., “A review targeting veterinary antibiotics removal from livestock manure
management systems and future outlook,” Bioresour. Technol., vol. 333, no. March, p. 125069, 2021, doi: 10.1016/j.biortech.2021.125069.
S. Wahjuni, S. H. Sanjiwo, and A. R. Akbar, “The Development of Chicken Coop Automatic
Remote Visual Monitoring System,” vol. 9, no. 2, pp. 161–168, 2022, doi: 10.15294/sji.v9i2.34630.
M. David, S. R. Sulistiyanti, H. Herlinawati, and H. Fitriawan, “Rancang Bangun Prototipe
Kandang Kambing Sistem Terkoleksi dan Pemberian Pakan Otomatis Berbasis Arduino Uno R3,”
J. Inform. dan Tek. Elektro Terap., vol. 10, no. 2, Apr. 2022, doi: 10.23960/jitet.v10i2.2442
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