Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities
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...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10433503/ |
| _version_ | 1797302651493285888 |
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| author | Melvin Kisten Absalom El-Shamir Ezugwu Micheal O. Olusanya |
| author_facet | Melvin Kisten Absalom El-Shamir Ezugwu Micheal O. Olusanya |
| author_sort | Melvin Kisten |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T23:41:32Z |
| format | Article |
| id | doaj.art-9192f8b395c94382bc47e9399973e272 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T23:41:32Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9192f8b395c94382bc47e9399973e2722024-02-20T00:01:26ZengIEEEIEEE Access2169-35362024-01-0112243482436710.1109/ACCESS.2024.336558610433503Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural FacilitiesMelvin Kisten0https://orcid.org/0000-0001-7051-8575Absalom El-Shamir Ezugwu1https://orcid.org/0000-0002-3721-3400Micheal O. Olusanya2https://orcid.org/0000-0002-8854-7822Unit for Data Science and Computing, North-West University, Potchefstroom, South AfricaUnit for Data Science and Computing, North-West University, Potchefstroom, South AfricaDepartment of Computer Science and Information Technology, Sol Plaatje University, Kimberley, South AfricaArtificial 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.https://ieeexplore.ieee.org/document/10433503/Agriculturesmart agricultural facilitiespredictive maintenancemachine learningdeep learningexplainable artificial intelligence |
| spellingShingle | Melvin Kisten Absalom El-Shamir Ezugwu Micheal O. Olusanya Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities IEEE Access Agriculture smart agricultural facilities predictive maintenance machine learning deep learning explainable artificial intelligence |
| title | Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities |
| title_full | Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities |
| title_fullStr | Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities |
| title_full_unstemmed | Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities |
| title_short | Explainable Artificial Intelligence Model for Predictive Maintenance in Smart Agricultural Facilities |
| title_sort | explainable artificial intelligence model for predictive maintenance in smart agricultural facilities |
| topic | Agriculture smart agricultural facilities predictive maintenance machine learning deep learning explainable artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10433503/ |
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