Predicting Artificial Intelligence Awareness among Agricultural Professionals Using Random Forest and SHAP Analysis

Ashok Kumar; Harshini Bathula; Ajay Kumar  Prusty; Akkamahadevi  Naik; Bikramjeet  Ghose

doi:10.48165/IJEE.2026.62333

Authors

Ashok Kumar M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Odisha
Bathula Harshini M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Odisha
Ajay Kumar Prusty M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Odisha
Akkamahadevi Naik M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Odisha
Bikramjeet Ghose M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Odisha

https://doi.org/10.48165/IJEE.2026.62333

Keywords:

Artificial Intelligence, Awareness, Agricultural Professionals, Interaction analysis, Correlation analysis.

Abstract

Agriculture plays an important role in economic development, rural livelihoods and food security. The increasing integration of artificial intelligence (AI) technologies in agriculture has enhanced productivity, decision-making and resource management. The present study was conducted during 2024-2025 among agricultural professionals working under Acharya N. G. Ranga Agricultural University (ANGRAU) in Andhra Pradesh to assess their awareness of AI and identify the factors influencing it. Correlation analysis, Random Forest regression, Permutation Feature Importance (PFI), SHAP analysis (SHapley Additive exPlanations), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE) and interaction analysis were employed for data analysis. The findings revealed that agricultural professionals possessed a moderately high level of AI awareness. Technical exposure-related factors, particularly familiarity with AI tools, practical application of AI technologies, participation in seminars and training programmes, and media exposure, emerged as the most influential predictors of AI awareness. The interpretability analyses further demonstrated that technology-oriented variables contributed more strongly to awareness levels than demographic characteristics. The study highlights the importance of strengthening capacity-building initiatives, experiential learning opportunities and digital extension programmes to enhance AI readiness among agricultural professionals and support the effective integration of AI-based innovations in agricultural extension systems.

Author Biographies

Bathula Harshini, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Odisha

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Ajay Kumar Prusty, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Odisha

NA
Akkamahadevi Naik, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Odisha

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Bikramjeet Ghose, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Odisha

NA

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