research-article

Smart Farming: : An Enhanced Pursuit of Sustainable Remote Livestock Tracking and Geofencing Using IoT and GPRS

Authors:

Qazi Mudassar Ilyas,

Muneer Ahmad Academic Editor:

Mohammad Hossein AnisiAuthors Info & Claims

Wireless Communications and Mobile Computing, Volume 2020

https://doi.org/10.1155/2020/6660733

Published: 01 January 2020 Publication History

Abstract

The farmers of agricultural farms manage and monitor different types of livestock. The manual inspection and monitoring of livestock are tedious since the cattle do not stay at fixed locations. Fencing many cattle requires a considerable cost and involves farmers’ physical intervention to keep an eye to stop them from crossing beyond the access points. Visual tracking of livestock and fencing is a time-consuming and challenging job. This research proposes a smart solution for livestock tracking and geofencing using state-of-the-art IoT technology. The study creates a geographical safe zone for cattle based on IoT and GPRS, where the cattle are assigned dedicated IoT sensors. The cattle can be easily remotely monitored and controlled without having any need for farmers to intervene for livestock management physically. The smart system collects the data regarding the location, well-being, and health of the livestock. This kind of livestock management may help prevent the spread of COVID-19, lower the farming costs, and enable remote monitoring.

References

[1]

S. Li and X. Li, “Global understanding of farmland abandonment: a review and prospects,” Journal of Geographical Sciences, vol. 27, no. 9, pp. 1123–1150, 2017.

[2]

A. Nikitas, K. Michalakopoulou, E. T. Njoya, and D. Karampatzakis, “Artificial intelligence, transport and the smart city: definitions and dimensions of a new mobility era,” Sustainability, vol. 12, no. 7, p. 2789, 2020.

[3]

H. Zahmatkesh and F. Al-Turjman, “Fog computing for sustainable smart cities in the IoT era: caching techniques and enabling technologies - an overview,” Sustainable Cities and Society, vol. 59, p. 102139, 2020.

[4]

A. A. Javadi and M. Rezania, “Applications of artificial intelligence and data mining techniques in soil modeling,” Geomechanics and Engineering, vol. 1, no. 1, pp. 53–74, 2009.

[5]

N. Kim, K. J. Ha, N. W. Park, J. Cho, S. Hong, and Y. W. Lee, “A comparison between major artificial intelligence models for crop yield prediction: case study of the Midwestern United States, 2006–2015,” ISPRS International Journal of Geo-Information, vol. 8, no. 5, p. 240, 2019.

[6]

R. S. Alonso, I. Sittón-Candanedo, Ó. García, J. Prieto, and S. Rodríguez-González, “An intelligent edge-IoT platform for monitoring livestock and crops in a dairy farming scenario,” Ad Hoc Networks, vol. 98, p. 102047, 2020.

Digital Library

[7]

H. Afzaal, A. A. Farooque, F. Abbas, B. Acharya, and T. Esau, “Computation of evapotranspiration with artificial intelligence for precision water resource management,” Applied Sciences, vol. 10, no. 5, p. 1621, 2020.

[8]

S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, vol. 7, 2016.

[9]

N. Larios, H. Deng, W. Zhang, M. Sarpola, J. Yuen, R. Paasch, A. Moldenke, D. A. Lytle, S. R. Correa, E. N. Mortensen, L. G. Shapiro, and T. G. Dietterich, “Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects,” Machine Vision and Applications, vol. 19, no. 2, pp. 105–123, 2008.

Digital Library

[10]

M. O. Adebiyi, R. O. Ogundokun, and A. A. Abokhai, “Machine learning-based predictive farmland optimization and crop monitoring system,” Scientifica, vol. 2020, 12 pages, 2020.

[11]

T. van Klompenburg, A. Kassahun, and C. Catal, “Crop yield prediction using machine learning: a systematic literature review,” Computers and Electronics in Agriculture, vol. 177, p. 105709, 2020.

[12]

P. Asghari, A. M. Rahmani, and H. H. S. Javadi, “Internet of things applications: a systematic review,” Computer Networks, vol. 148, pp. 241–261, 2019.

[13]

J. M. Talavera, L. E. Tobón, J. A. Gómez, M. A. Culman, J. M. Aranda, D. T. Parra, L. A. Quiroz, A. Hoyos, and L. E. Garreta, “Review of IoT applications in agro-industrial and environmental fields,” Computers and Electronics in Agriculture, vol. 142, pp. 283–297, 2017.

Digital Library

[14]

R. Dolci, “IoT solutions for precision farming and food manufacturing: artificial intelligence applications in digital food,” in 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), Turin, Italy, 2017.

[15]

L. Atzori, A. Iera, and G. Morabito, “The internet of things: a survey,” Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010.

Digital Library

[16]

S. Li, L. Da Xu, and S. Zhao, “The internet of things: a survey,” Information Systems Frontiers, vol. 17, no. 2, pp. 243–259, 2015.

Digital Library

[17]

L. Da Xu, W. He, and S. Li, “Internet of things in industries: a survey,” IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233–2243, 2014.

[18]

A. Patrik, G. Utama, A. A. S. Gunawan, A. Chowanda, J. S. Suroso, R. Shofiyanti, and W. Budiharto, “GNSS-based navigation systems of autonomous drone for delivering items,” Journal of Big Data, vol. 6, no. 1, 2019.

[19]

W. Ruan, Q. Z. Sheng, L. Yao, T. Gu, M. Ruta, and L. Shangguan, “Device-free indoor localization and tracking through human-object interactions,” in 2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), Coimbra, Portugal, 2016.

[20]

I. Daugela, J. Sužiedelyte Visockiene, and V. Česlovas Aksamitauskas, “Erratum to: RPAS and GIS for landfill analysis,” E3S Web of Conferences, vol. 44, article 00203, 2018.

[21]

M. Wang, X. Liu, Y. Zhang, and Z. Wang, “Camera coverage estimation based on multistage grid subdivision,” ISPRS International Journal of Geo-Information, vol. 6, no. 4, p. 110, 2017.

[22]

G. Q. Tao, X. K. Ou, Y. M. Guo, Q. Xu, Q. C. Yu, Z. M. Zhang, and C. Y. Wang, “Priority area identification for vegetation in Northwest Yunnan, based on protection value and protection cost,” Acta Ecologica Sinica, vol. 36, no. 18, 2016.

[23]

I. Halachmi, A. S. Tello, A. P. Fernández, T. van Hertem, V. Sibony, S. Weyl-Feinstein, A. Verbrugge, M. Bonneau, and R. Neilson, “6.4. Discussion: PLF for automatic detection of animal health in cows,” in Precision livestock farming applications, 2015.

[24]

A. Spink, B. Cresswell, A. Kölzsch, F. Van Langevelde, M. Neefjes, L. P. J. J. Noldus, H. Van Oeveren, H. Prins, T. Van Der Wal, N. De Weerd, and W. F. De Boer, “Animal behaviour analysis with GPS and 3D accelerometers,” in Precision Livestock Farming 2013 - Papers Presented at the 6th European Conference on Precision Livestock Farming, ECPLF 2013, Leuven, Belgium, 2013.

[25]

J. Wall, G. Wittemyer, B. Klinkenberg, and I. Douglas-Hamilton, “Novel opportunities for wildlife conservation and research with real-time monitoring,” Ecological Applications, vol. 24, no. 4, pp. 593–601, 2014.

[26]

M. Benjamin and S. Yik, “Precision livestock farming in swine welfare: a review for swine practitioners,” Animals, vol. 9, no. 4, p. 133, 2019.

[27]

S. Neethirajan, S. K. Tuteja, S. T. Huang, and D. Kelton, “Recent advancement in biosensors technology for animal and livestock health management,” Biosensors and Bioelectronics, vol. 98, pp. 398–407, 2017.

[28]

J. K. Siror, S. Huanye, W. Dong, and W. Jie, “Use of RFID technologies to combat cattle rustling in the East Africa,” in 2009 Fifth International Joint Conference on INC, IMS and IDC, Seoul, South Korea, 2009.

Digital Library

[29]

P. Wamuyu, “A conceptual framework for implementing a WSN based cattle recovery system in case of cattle rustling in Kenya,” Technologies, vol. 5, no. 3, p. 54, 2017.

[30]

H. Bouazza, O. Zerzouri, M. Bouya, A. Charoub, and A. Hadjoudja, “A novel RFID system for monitoring livestock health state,” in 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, 2018.

[31]

A. Carabús, M. Gispert, and M. Font-i-Furnols, “Imaging technologies to study the composition of live pigs: a review,” Spanish Journal of Agricultural Research, vol. 14, no. 3, 2016.

[32]

I. Kröger, E. Humer, V. Neubauer, N. Kraft, P. Ertl, and Q. Zebeli, “Validation of a noseband sensor system for monitoring ruminating activity in cows under different feeding regimens,” Livestock Science, vol. 193, pp. 118–122, 2016.

[33]

G. Mattachini, E. Riva, F. Perazzolo, E. Naldi, and G. Provolo, “Monitoring feeding behaviour of dairy cows using accelerometers,” Journal of Agricultural Engineering, vol. 47, no. 1, p. 54, 2016.

[34]

A. Peña Fernández, T. Norton, E. Tullo, T. van Hertem, A. Youssef, V. Exadaktylos, E. Vranken, M. Guarino, and D. Berckmans, “Real-time monitoring of broiler flock’s welfare status using camera-based technology,” Biosystems Engineering, vol. 173, pp. 103–114, 2018.

[35]

B. Xu, W. Wang, G. Falzon, P. Kwan, L. Guo, Z. Sun, and C. Li, “Livestock classification and counting in quadcopter aerial images using mask R-CNN,” International Journal of Remote Sensing, vol. 41, no. 21, pp. 8121–8142, 2020.

[36]

U. McCarthy, L. Brennan, S. Ward, and G. Corkery, “Enhanced efficiencies in the poultry industry via real-time monitoring and cloud-enabled tracking,” in Precision Livestock Farming 2013 - Papers Presented at the 6th European Conference on Precision Livestock Farming, ECPLF 2013, pp. 212–222, Leuven, Belgium, 2013.

[37]

N. A. Molapo, R. Malekian, and L. Nair, “Real-time livestock tracking system with integration of sensors and beacon navigation,” Wireless Personal Communications, vol. 104, no. 2, pp. 853–879, 2019.

Digital Library

[38]

K. E. Veblen, D. A. Pyke, C. L. Aldridge, M. L. Casazza, T. J. Assal, and M. A. Farinha, “Range-wide assessment of livestock grazing across the sagebrush biome,” U.S. Geological Survey Open-File Report 2011-1263, 2011.

[39]

L. Germani, V. Mecarelli, G. Baruffa, L. Rugini, and F. Frescura, “An IoT architecture for continuous livestock monitoring using lora LPWAN,” Electronics, vol. 8, no. 12, p. 1435, 2019.

[40]

U. S. Abdullahi, M. Nyabam, K. Orisekeh, S. Umar, B. Sani, E. David, and A. A. Umoru, “Exploiting IoT and LoRaWAN technologies for effective livestock monitoring in Nigeria,” Arid Zone Journal of Engineering, Technology and Environment, vol. 15, pp. 146–159, 2019.

[41]

E. A. Raizman, H. B. Rasmussen, L. E. King, F. W. Ihwagi, and I. Douglas-Hamilton, “Feasibility study on the spatial and temporal movement of Samburu’s cattle and wildlife in Kenya using GPS radio-tracking, remote sensing and GIS,” Preventive Veterinary Medicine, vol. 111, no. 1-2, pp. 76–80, 2013.

[42]

P. E. Clark, D. E. Johnson, M. A. Kniep, P. Jermann, B. Huttash, A. Wood, M. Johnson, C. McGillivan, and K. Titus, “An advanced, low-cost, GPS-based animal tracking system,” Rangeland Ecology & Management, vol. 59, no. 3, pp. 334–340, 2006.

[43]

D. L. Swain, M. A. Friend, G. J. Bishop-Hurley, R. N. Handcock, and T. Wark, “Tracking livestock using global positioning systems are we still lost?” Animal Production Science, vol. 51, no. 3, p. 167, 2011.

[44]

H. Homburger, A. Lüscher, M. Scherer-Lorenzen, and M. K. Schneider, “Patterns of livestock activity on heterogeneous subalpine pastures reveal distinct responses to spatial autocorrelation, environment and management,” Movement Ecology, vol. 3, no. 1, 2015.

[45]

Y. Duan, L. Ma, and G. Liu, “Remote monitoring system of pig motion behavior and piggery environment based on internet of things,” Transactions of the Chinese Society of Agriculture Engineering, vol. 31, pp. 216–221, 2015.

[46]

M. Hashim, S. Misbari, and A. B. Pour, “Landslide mapping and assessment by integrating Landsat-8, PALSAR-2 and GIS techniques: a case study from Kelantan State, Peninsular Malaysia,” Journal of the Indian Society of Remote Sensing, vol. 46, no. 2, pp. 233–248, 2018.

[47]

R. R. Miller, Utilizing GIS and remote sensing to determine sheep grazing patterns for best practices in land management protocols, [Ph.D. thesis], ProQuest Dissertations and Theses, 2012.

[48]

H. Q. T. Ngo, T. P. Nguyen, and H. Nguyen, “Research on a low-cost, open-source, and remote monitoring data collector to predict livestock’s habits based on location and auditory information: a case study from Vietnam,” Agriculture, vol. 10, no. 5, p. 180, 2020.

[49]

K. K. Benke, F. Sheth, K. Betteridge, C. J. Pettit, and J. P. Aurambout, “A geo-visual analytics approach to biological shepherding: modelling animal movements and impacts,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. I-2, pp. 117–122, 2012.

[50]

H. Lei and L. Yang, “Research and design of technology for tracking and positioning wild stocking animals,” Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, vol. 23, 2014.

[51]

V. Maria Anu and R. Aroul Canessane, “Livestock monitoring using RFID with R+ tree indexing,” Biomedical Research, vol. 28, no. 6, 2017.

[52]

D. W. Bailey and J. R. Brown, “Rotational grazing systems and livestock grazing behavior in shrub-dominated semi-arid and arid rangelands,” Rangeland Ecology & Management, vol. 64, no. 1, pp. 1–9, 2011.

[53]

D. C. Ganskopp and D. W. Bohnert, “Landscape nutritional patterns and cattle distribution in rangeland pastures,” Applied Animal Behaviour Science, vol. 116, no. 2-4, pp. 110–119, 2009.

[54]

D. B. Lindenmayer, W. Blanchard, M. Crane, D. Michael, and C. Sato, “Biodiversity benefits of vegetation restoration are undermined by livestock grazing,” Restoration Ecology, vol. 26, no. 6, pp. 1157–1164, 2018.

Cited By

Habib MKabir MZheng LMcGrath S(2024) LEIComputers and Electronics in Agriculture10.1016/j.compag.2024.108874220:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.108874
Li JLing MShui JHuang SDan JGou BWu Y(2022)Smart Grazing in Tibetan PlateauWireless Communications & Mobile Computing10.1155/2022/18700942022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1870094
Sheham MHassan MImran AAhmed TParvez MAhmed A(2022)Design of an IoT-based Smart Farming SystemProceedings of the 2nd International Conference on Computing Advancements10.1145/3542954.3542996(284-293)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1145/3542954.3542996

Index Terms

Smart Farming: An Enhanced Pursuit of Sustainable Remote Livestock Tracking and Geofencing Using IoT and GPRS

Index terms have been assigned to the content through auto-classification.

Recommendations

Crop mapping in countries with small-scale farming: a case study for West Shewa, Ethiopia

Remote sensing is nowadays considered to be a valuable input for the annual collection of crop statistics. Derived crop maps can serve as a baseline for yield or area estimation or to target next year's census. For subsistence farming, where small ...
Smart farming using artificial intelligence: A review
Abstract
Smart farming with artificial intelligence provides an efficient solution to today’s agricultural sustainability challenges. Machine learning, Deep learning, and time series analysis are essential in smart farming. Crop selection, crop ...
Highlights
- Different technologies such as machine learning, deep learning, and time series analysis have been presented.
GIS-based study of the spatial distribution suitability of livestock and poultry farming

The spatial distribution suitability is important for the sustainable development.Suitability analysis is a GIS-based multi-criteria decision making process.We integrate GIS and AHP technique for spatial distribution suitability analysis.We establish a ...

Comments

Information & Contributors

Information

Published In

cover image Wireless Communications & Mobile Computing

Wireless Communications & Mobile Computing Volume 2020, Issue

2020

4630 pages

ISSN:1530-8669

Issue’s Table of Contents

Copyright © 2020 Qazi Mudassar Ilyas and Muneer Ahmad.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Publisher

John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 01 January 2020

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Habib MKabir MZheng LMcGrath S(2024) LEIComputers and Electronics in Agriculture10.1016/j.compag.2024.108874220:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.108874
Li JLing MShui JHuang SDan JGou BWu Y(2022)Smart Grazing in Tibetan PlateauWireless Communications & Mobile Computing10.1155/2022/18700942022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1870094
Sheham MHassan MImran AAhmed TParvez MAhmed A(2022)Design of an IoT-based Smart Farming SystemProceedings of the 2nd International Conference on Computing Advancements10.1145/3542954.3542996(284-293)Online publication date: 10-Mar-2022
https://dl.acm.org/doi/10.1145/3542954.3542996

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents