Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

Enhancing Predictive Maintenance in Smart Agriculture using Explainable Artificial Intelligence

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  • T Prathima Reddy,
  • R Prathyusha,
  • S Kamal Basha,
  • Sasikumar Reddy,
  • S Mohammed Jabeer
T Prathima Reddy
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
R Prathyusha
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Kamal Basha
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
Sasikumar Reddy
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
S Mohammed Jabeer
Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15250635

Published 2025-04-20

Keywords

  • AI,
  • Agriculture,
  • prediction mode

How to Cite

T Prathima Reddy, R Prathyusha, S Kamal Basha, Sasikumar Reddy, & S Mohammed Jabeer. (2025). Enhancing Predictive Maintenance in Smart Agriculture using Explainable Artificial Intelligence. International Journal of Computational Learning & Intelligence, 4(4), 658–671. https://doi.org/10.5281/zenodo.15250635

Copyright (c) 2025 T Prathima Reddy, R Prathyusha, S Kamal Basha, Sasikumar Reddy, S Mohammed Jabeer

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

The integration of Artificial Intelligence (AI) in Smart Agricultural Facilities (SAF) enhances efficiency but often lacks transparency, limiting its adoption by farmers. This study introduces a Predictive Maintenance (PdM) model powered by Explainable Artificial Intelligence (XAI) to improve both predictive accuracy and interpretability. The proposed model offers explanations across four key dimensions: data, model, outcome, and end-user, ensuring better understanding and usability for stakeholders. Experimental results demonstrate that the Long Short-Term Memory (LSTM) classifierimproves accuracy by 5.81%, while the eXtreme Gradient Boosting (XGBoost) classifier achieves a 7.09% increase in F1 score, 10.66% higher accuracy, and a 4.29% improvement in ROC-AUC. These enhancements lead to more precise maintenance predictions, reducing costs and improving reliability in SAF. Additionally, this study highlights data integrity, global and local model explanations, and counterfactual reasoning to enhance transparency in AI-driven PdM. By emphasizing interpretability beyond conventional accuracy metrics, this research contributes to advancing trustworthy AI applications in agriculture. Future research should explore multi-modal data integration and Human-in-the-Loop (HITL) systems to address ethical concerns such as Fairness, Accountability, and Transparency (FAT) in AI-driven agricultural technologies.

 

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