Reliability of Agricultural Information Generated by Researchers through AI Platforms in Forest Research Institute of Nigeria
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Abstract
Human research can suffer from perspective or intuitive bias, while AI outputs depend on the quality of training data, risking faulty generalizations in research and technical writing. This study assessed the reliability of agricultural information generated by researchers using AI platforms at FRIN. Objectives were to describe respondents’ socio-economic characteristics, identify AI platforms available and used, examine agricultural information generated via AI, and assess constraints to accuracy and reliability. Data were collected from 62 FRIN researchers using structured questionnaires and analyzed with frequency counts, percentages, means, and Pearson Product Moment Correlation (PPMC). The mean age of respondents was 34 years. Google Earth Search, ChatGPT, and Gemini were the most available and used AI platforms. AI-generated information on forest extension services, new forestry technologies, and climate-smart agriculture showed high reliability. PPMC results showed significant relationships between high cost of maintenance (r = 0.374, p = 0.003), extent of AI complexity (r = 0.351, p = 0.005), and the reliability of AI-generated agricultural information. The study concluded that several AI platforms have strong potential to improve agricultural research by providing reliable information on forest extension and new forestry technologies. However, power instability and privacy/security concerns remain key challenges to reliability. It recommends that AI platform developers integrate robust cybersecurity protocols such as data encryption, secure authentication, and regular system audits to protect user data and build trust among agricultural researchers and extension workers.
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Copyright (c) 2026 Oyedele Temitope Charles, Jegede, Comfort Odunayo, Olawoore Titilayo, Prof. Oyetoro John Oyewole (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Oyedele Temitope Charles, Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria.
Department of Agricultural Extension and Rural Development,
Faculty of Agricultural Sciences.
Jegede, Comfort Odunayo, Ajayi Crowther University, Oyo, Nigeria.
Department of Agricultural Economics and Extension.
Olawoore Titilayo, Forestry Research Institute of Nigeria, Jericho Ibadan, Nigeria.
Department of Forest Economics and Extension Services.
Prof. Oyetoro John Oyewole, Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria.
Professor (Department of Agricultural Extension and Rural Development, Faculty of Agricultural
Sciences, Ladoke Akintola University of Technology (LAUTECH), Ogbomoso, Nigeria.)
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