Utilisation of Artificial Intelligence-based Technology for Agricultural Extension Services among Extension Professionals in Nigeria

Utilisation of Artificial Intelligence-based Technology for Agricultural Extension Service in Nigeria

Authors

  • Olanike Fasilat Deji Obafemi Awolowo University Ile-Ife
  • D. L. Alabi
  • Michael Famakinwa Obafemi Awolowo University ile-Ife
  • E. B. Faniyi Department of Agricultural Extension and Management Rufus Giwa Polytechnic, Owo, Ondo State, Nigeria

Keywords:

Artificial intelligence, digital technology, artificial intelligence for agricultural extension services

Abstract

The study examined the current awareness and usage, determined the level of utilisation of AI-based digital technology for agricultural extension services, and identified the merit and demerit of using AI-based digital technology for agricultural extension services. Data were collected through an online structured questionnaire from 131 extension professionals across Nigeria. Percentage and mean were used to describe and summarise the data. The findings revealed that 79.4% of the respondents were aware that AI-based digital technology can be used for agricultural extension services, while 55.7% reported that they had used the technology at one time or the other. About 45% of the respondents disseminated innovations and 34% demonstrated innovations and technologies through the use of AI-based digital technology. Also, 77.9% perceived reaching the target audience everywhere and every time as the major merit of AI-based digital technology while 71.8% identified high-cost implications of digital enablers as its major demerit. There was a high level of awareness but a low level of utilization of AI-based digital technology for agricultural extension services among agricultural extension professionals. On-the-job capacity building should be organized for the current professionals to promote the use of AI-based digital technology for agricultural extension services in Nigeria

Author Biographies

Olanike Fasilat Deji, Obafemi Awolowo University Ile-Ife

Dept of Agricultural Extension and Rural Development, Obafemi Awolowo University Ile-Ife

D. L. Alabi

Department of Agricultural Extension and Rural Development Obafemi Awolowo University, Ile Ife, Nigeria

E. B. Faniyi, Department of Agricultural Extension and Management Rufus Giwa Polytechnic, Owo, Ondo State, Nigeria

Department of Agricultural Extension and Management Rufus Giwa Polytechnic, Owo, Ondo State, Nigeria

References

References

Adesoji, S. A., Famakinwa, M. and Eghosa, A. E. (2019). Assessment of agricultural extension students’ interest in providing private extension services in Nigeria. Journal of Agricultural Science. 14(1):57-66.

Adilakshmi, G, Chaitany, A, Poojitha, K. and Ashok Naik, M. (2021). Application of artificial intelligence in agriculture, Just Agriculture 1(10):1-3

Adisa, B. O., Famakinwa. M., Adeloye, K. A., and Adigun, A. O. (2022). Crop farmers’ coping strategies for mitigating conflicts with cattle herders: Evidence from Osun State, Nigeria. Agricultura Tropica et Subtropica, 55 OV, 191–201

Camillone, N., Duiker, S., Bruns., M. A., Onyibe, J. and Omotayo, A (2020). Context, Challenges, and Prospects for Agricultural Extension in Nigeria. Journal of International Agricultural and Extension Education. 27(4):144-156

Centre for Agricultural and Rural Cooperation (CTA) (2019). The digitalisation of African

agriculture report 2018–2019, 1st Edition, June 2019

Danso-Abbeam, G., Ehiakpor, D. S., & R. Aidoo (2018). Agricultural extension and its effects on farm productivity and income: insight from Northern Ghana. Agriculture & Food Security, 7(1), 1-10.

Fatty, L. K. (2019). Agricultural Extension Services Delivery and Post-Harvest Losses of Horticultural Crop Produce in West Coast Region of the Gambia (Doctoral dissertation).

Farinde, A. J., Ojo, T. F. and Famakinwa, M. (2022). Virtual and Artificial Intelligence Tools for Extension Practices in Nigeria in Agricultural Extension In Nigeria. 3rd edition, Agricultural Extension Society of Nigeria, Pg.165-180

https://www.javatpoint.com/artificial-intelligence-in-agriculture Artificial Intelligence in Agriculture accessed on 26, Sept. 2022

Jani, K., Chaudhuri, M., Patel, H. & M. Shah (2020). Machine learning in films: an approach towards automation in film censoring. Journal of Data, Information and Management 2, 55–64

Liakos, K. G., Busato, P., Moshou, D., Pearson, S. and D. Bochtis (2018). Machine Learning in Agriculture: A Review. Sensors, 18 (8): 2674

Maertens, A., Michelson, H., & Nourani, V. (2020). How do farmers learn from extension

services? evidence from Malawi. American Journal of Agricultural Economics, 103(2): 569-595.

Miiro R., Luzobe, B., Mangheni, M.and Asiimwe, A. (2020). Responding to the COVID-19 Lockdown, Agricultural Extension Agents’ Experiences in Uganda: A Survey by the Uganda Forum for Agricultural Advisory Services (UFAAS). Food and Agriculture Organization (FAO) Webinar series, June 2020.

Nikola, M. T., Samuel, V. and Meng, Z. (2019). Digital Technologies in Agriculture and Rural Areas Briefing Paper. Food and Agriculture Organization of the United Nations Rome. https://www.fao.org/3/ca4887en/ca4887en.pdf.

Olagunju, O., Adetarami, O., Koledoye G., Olumoyegun, A and Nabara, I. (2021). Digitization of Agricultural Extension System for Effective Management of Emergency in Nigeria. Journal of Agricultural Extension, 25 (4): 81-91.

Parekh, V., Shah, D. & M. Shah (2020). Fatigue detection using artificial intelligence

framework. Augmented Human Research, (5), p. 5

Prava, J. (2020). Artificial Intelligence in Agriculture: Using Modern-Day AI to Solve

Traditional Farming Problems. Accessed on 21 February 2022 fromhttps://www.analyticsvidhya.com/blog/2020/11/artificial-intelligence-in-agriculture-using-modern-day-ai-to-solve-traditional-farming-problems/

Rakhra, M., Sumaya, S., Quadri,N. N.,Verma, N., Ray, S., and Asenso, E (2022).Implementing Machine Learning for Smart Farming to Forecast Farmers' Interest in Hiring Equipment. Hindawi Journal of Food Quality, Volume 2022, Article ID 4721547, 17 pages Accessed on 21 February 2022 from https://doi.org/10.1155/2022/4721547

Ridley M., G. Rao, P. Vikram, F. Schilbach (2019). Poverty and Mental Illness: Causal Evidence. https://economics.mit.edu/files/18694

Rotondia, V., Kashyapa, R., Pesandoe, L. M., Spinellib, S. and Billarib, F. C. (2020). Leveraging mobile phones to attain sustainable development. PNAS, 117(24):13413–13420 www.pnas.org/cgi/doi/10.1073/pnas.1909326117

Sarker, M. N. I, Islam M. S, Ali. M. A, Islam, M. S, Salam. M. A, and Mahmud, S. M. H (2019). Promoting digital agriculture through big data for sustainable farm management. International Journal of Innovation and Applied Studies 25(4): 1235-1240

Silva, G. (2018). Feeding the World in 2050 and beyond – Part 1: Productivity Challenges. Michigan State University Extension - December 3, 2018.

Singh, S., and Jain, P. (2022). Applications of Artificial Intelligence for the Development of Sustainable Agriculture. In: Kumar, P., Tomar, R.S., Bhat, J.A., Dobriyal, M., Rani, M. (eds) Agro-biodiversity and Agri-ecosystem Management. Springer, Singapore. https://doi.org/10.1007/978-981-19-0928-3_16

Statista (2022). Forecasted population in Nigeria in selected years between 2025 and 2050. Accessed on July 28th, 2022 from

https://www.statista.com/statistics/1122955/forecasted-population-in-nigeria/#:~:text=By%202050%2C%20it%20is%20forecast,reaching%20over%20400%20million%20people.

Talaviya, T., Shah, D., Patel, N., Yagnik, H. and Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides, Artificial Intelligence in Agriculture, 4, 58-73 Accessed on 2nd February 2023 from https://doi.org/10.1016/j.aiia.2020.04.002.

Trendov, N. M., Varas, S. & M. Zeng (2019). Digital technologies in agriculture and rural

areas – Status report. Rome. Accessed on December 28th, 2022 from http://www.fao.org/3/ca4985en/ca498

Tsan, M., Totapally, S., Hailu, M., and Addom, B. K. (2019). The Digitalisation of African Agriculture Report 2018 – 2019. CTA 2019, 1st Edition, June 2019. Proud Press, The Netherlands.

United Nations Population Fund (UNFPA) (2021). Nigeria now has 211 million people - UNFPA report. Accessed on March 28th, 2023 from t https://www.ghanaweb.com/GhanaHomePage/africa/Nigeria-now-has-211-million-people-UNFPA-report-1282117

World Population Data Sheet (2020). Accessed on March 28th, 2023 from https://interactives.prb.org/2020-wpds/

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Published

2023-07-23

How to Cite

Deji, O. F., Famakinwa, D. L., Famakinwa, M., & Faniyi, E. O. (2023). Utilisation of Artificial Intelligence-based Technology for Agricultural Extension Services among Extension Professionals in Nigeria: Utilisation of Artificial Intelligence-based Technology for Agricultural Extension Service in Nigeria. Journal of Agricultural Extension, 27(3), 81–90. Retrieved from https://aesonnigeria.org/ajm/index.php/jae/article/view/3749

Issue

Section

General Extension and Teaching Methods