msc-research-project-implementation

Repository for my msc-research-project-implementation project

Overview

Predictive Maintenance (PdM) and eXplainable Artificial Intelligence (XAI) illustration

This repository supports the paper "Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities," which can be found at the following link: Open Access Paper Link

Abstract

Artificial Intelligence (AI) in Smart Agricultural Facilities (SAF) often lacks explainability, hindering farmers from taking full advantage of their capabilities. This study tackles this gap by introducing a model that combines eXplainable Artificial Intelligence (XAI), with Predictive Maintenance (PdM). The model aims to provide both predictive insights and explanations across four key dimensions, namely data, model, outcome, and end-user. This approach marks a shift in agricultural AI, reshaping how these technologies are understood and applied. The model outperforms related studies, showing quantifiable improvements. Specifically, the Long-Short-Term Memory (LSTM) classifier shows a 5.81% rise in accuracy. The eXtreme Gradient Boosting (XGBoost) classifier exhibits a 7.09% higher F1 score, 10.66% increased accuracy, and a 4.29% increase in Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). These results could lead to more precise maintenance predictions in real-world settings. This study also provides insights into data purity, global and local explanations, and counterfactual scenarios for PdM in SAF. It advances AI by emphasising the importance of explainability beyond traditional accuracy metrics. The results confirm the superiority of the proposed model, marking a significant contribution to PdM in SAF. Moreover, this study promotes the understanding of AI in agriculture, emphasising explainability dimensions. Future research directions are advocated, including multi-modal data integration and implementing Human-in-the-Loop (HITL) systems aimed at improving the effectiveness of AI and addressing ethical concerns such as Fairness, Accountability, and Transparency (FAT) in agricultural AI applications.

Written in R, Python and PlantUML

1. Methodologies/Project Management:

   • Agile

2. Coding Practices:

   • OOP (Object Oriented Programming)
   • MVC (Model View Controller)

3. Programming Languages/Frameworks:

   • R
   • Python
   • PlantUML - Unified Modeling Language (UML)

Instructions (For R section, check README.md in "r-imp")

Instructions (For Python section, check README.md in "py-imp")

Instructions (For PantUML section, check README.md in "plantuml-imp")

Instructions

1. Make sure you have these installed

2. Clone ONLY THE LATEST COMMIT of this repository into your local machine using the terminal (mac) or Gitbash (PC) to save storage space

   git clone https://github.com/iammelvink/msc-research-project-2023.git --depth=1

Citation

If you use this code or find the paper helpful, please cite it as follows:

@article{10433503,
   title = {Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities},
   author = {Kisten, Melvin and Ezugwu, Absalom El-Shamir and Olusanya, Micheal O.},
   journal = {IEEE Access},
   volume = {12},
   pages = {24348-24367},
   ISSN = {2169-3536},
   DOI = {10.1109/ACCESS.2024.3365586},
   year = {2024},
   type = {Journal Article}
}

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