Bridging Extension Gaps through Digital Advisory Systems: Lessons from a  Mobile Expert System for Rice Farmers in Nigeria

Authors

  • Ladan E O Africa Centre of Excellence on Technology Enhanced Learning, National Open University of Nigeria (ACETEL, NOUN), FCT Abuja, Nigeria.
  • Oyefolahan I O Department of Information and Computer Technology, National Open University of Nigeria, FCT Abuja, Nigeria.
  • Joseph S I Department of Computer Science, Ebonyi State University, Abakaliki, Ebonyi State, Nigeria.

DOI:

https://doi.org/10.48165/ijaee.2025.1.2.4

Keywords:

Rural Livelihoods, Digital Agriculture, Rice Farming

Abstract

Agricultural extension services remain unevenly distributed across rural Nigeria,  limiting smallholder farmers’ access to timely and context-specific agronomic  knowledge. This study examines the role of mobile-based expert systems as a  socio-technical intervention for strengthening agricultural extension in resource constrained rural settings. Using a Design Science Research approach, we developed  and evaluated RiceAdvisor, a mobile expert system designed to support rice disease  diagnosis and management among smallholder farmers. System requirements were  informed by survey data collected from 270 rice farmers across three agro-ecological  zones in Nigeria, highlighting critical gaps in disease diagnosis, extension access,  and decision support. The system integrates localized expert knowledge, rule-based  reasoning, multilingual interaction, and offline functionality to accommodate  rural infrastructural and literacy constraints. Usability evaluation with 50 farmers  produced a System Usability Scale score of 76.5, indicating acceptable to good usability  and perceived relevance. Findings suggest that mobile expert systems can enhance  farmers’ confidence, problem-solving capacity, and access to extension knowledge,  particularly in contexts where conventional advisory services are limited. The study  contributes to rural studies and agricultural extension services by demonstrating  how digital advisory tools can be designed and embedded within rural knowledge  systems to support sustainable agricultural practices in Nigeria.

References

Adedokun M, Alamu O, Abiona SO. Mobile-based cassava disease diagnostic system using rule-based approach. J Agric Inform. 2020;11(2):1-8.

Agus F, Ihsan M, Khairina DM, Candra KP. ES for RPD2: Expert system for rice plant disease diagnosis. F1000Res. 2018;7:1902. doi:10.12688/f1000research.16657.3

Alih L. Enhancing agricultural productivity in Nigeria through technological innovations: The study of predictive tools and smart farming. Int J Res Innov Soc Sci. 2024;8(12):1716-1733. doi:10.47772/IJRISS.2024.8120147

Baig F, Nawaz N, Rehman S. Expert systems for decision-making in agriculture sector. J Agric Soc Sci. 2005;1:208-211.

Bari BS, et al. A real-time approach of diagnosing rice leaf disease using deep learning-based Faster R-CNN framework. PeerJ Comput Sci. 2021;7:e432. doi:10.7717/peerj-cs.432

Benali S, Boukhalfa A. Development of a mobile-based expert system for rice disease diagnosis using forward chaining and real-time data integration. Int J Electron Devices Netw. 2024;5(2):42-47. doi:10.22271/27084477.2024.v5.i2a.65

Deng R, et al. Automatic diagnosis of rice diseases using deep learning. Front Plant Sci. 2021;12:701038. doi:10.3389/fpls.2021.701038

Dlodlo N, Kalezhi J. The internet of things in agriculture for sustainable rural development. In: Proc Int Conf Emerging Trends Networking, Computing and Communications (ETNCC). IEEE; 2015.

Eleke UP, et al. Influence of ICT-based training on livestock production efficiency in Bwari Area Council, Abuja. Glob Acad J Agric Biosci. 2024;6(6):199-207.

Emeana EM, Trenchard L, Dehnen-Schmutz K. The revolution of mobile phone-enabled services for agricultural development (m-Agri services) in Africa: The challenges for sustainability. Sustainability. 2020;12(2):485.

Grunfeld H, Houghton J. Using ICT for climate change adaptation and mitigation through agro-ecology in the developing world. In: Proc Int Conf ICT Sustain. ETH Zurich; 2013. p.128-137.

Jearanaiwongkul W, Anutariya C, Racharak T, Andres F. An ontology-based expert system for rice disease identification and control recommendation. Appl Sci. 2021;11(21):10450. doi:10.3390/app112110450

Khoerunisa Z, et al. Expert system for diagnosing diseases in rice plants. Adv Eng Res. 2024;230. doi:10.2991/978-94-6463-364-1_61

Lewis JR, Sauro J. Item benchmarks for the System Usability Scale. J Usability Stud. 2018;13(3):109-118.

Li R, et al. Predicting rice diseases using advanced technologies at different scales: Present status and future perspectives. aBIOTECH. 2023;4:359-371.

Naresh Kumar B, Sakthivel S. Rice leaf disease classification using a fusion vision approach. Sci Rep. 2025;15:8692. doi:10.1038/s41598-025-87800-3

Ogunlela YI, Mukhtar AA. Gender issues in agriculture and rural development in Nigeria: The role of women. Humanit Soc Sci J. 2009;4(1):19-30.

Okai G, Akinyede R, Agangiba M, Agangiba W. Development of a knowledge management system to support intelligent rice farming. J Sci Logics ICT Res. 2025;13(1):2714-2727.

PwC. Nigeria’s rice industry at a glance. PwC Analysis; 2018.

Sarku R, et al. Digital platforms in climate information service delivery for farming in Ghana. In: African handbook of climate change adaptation. Springer; 2021. p.1247-1277.

Sran A, Komiak S, Manzoor S. Farm-n-Pedia: Expert mobile agricultural knowledge-based system for Indian farmers. Int J Res Bus Soc Sci. 2021;10:27-39. doi:10.20525/ijrbs.v10i7.1437

United States Department of Agriculture. World agricultural production. USDA Foreign Agricultural Service; 2020.

Abdulai S, Zakariah A, Donkoh SA. Adoption of rice cultivation technologies and its effect on technical efficiency in Sagnarigu District of Ghana. Cogent Food Agric. 2018;4(1):1424296. doi:10.1080/23311932.2018.1424296

Ingram J, Maye D. What are the implications of digitalisation for agricultural knowledge? Front Sustain Food Syst. 2020;4. doi:10.3389/fsufs.2020.00066

Zheng J, et al. Exploring the usability, user experience and usefulness of a supportive website for people with dementia and carers. Disabil Rehabil Assist Technol. 2024;19(4):1369-1381. doi:10.1080/17483107.2023.2180546

Downloads

Published

2025-12-30

How to Cite

Bridging Extension Gaps through Digital Advisory Systems: Lessons from a  Mobile Expert System for Rice Farmers in Nigeria. (2025). International Journal of Agricultural Extension and Education, 1(2), 30-38. https://doi.org/10.48165/ijaee.2025.1.2.4