AI Summary & Key Takeaways
AI InsightThe review concludes that agro-food processing industries should prioritise IoT sensor integration, invest in AI-literacy training for maintenance personnel, and engage policy makers to develop supportive agricultural engineering technology frameworks.
📌 3 Key Takeaways
Abstract
Unplanned machine breakdowns resulting from overreliance on corrective maintenance represent one of the most significant operational challenges in agro-food processing industries. This review examines the transformative potential of artificial intelligence (AI) and machine learning (ML) in transitioning agro-food processing facilities from reactive corrective maintenance to proactive AI-driven predictive maintenance (AI-PdM). A structured review of peer-reviewed literature published between 2019 and 2025 was conducted across the Scopus, Web of Science, and Google Scholar databases using defined inclusion and exclusion criteria, yielding 24 articles that directly informed the findings. Key AI algorithms reviewed include Random Forest, Long Short-Term Memory (LSTM) neural networks, Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Gradient Boosting, and hybrid AI models, demonstrating predictive accuracy ranges of 85-96% in agro-food processing equipment failure prediction. An original three-layer AI-PdM Framework is proposed, comprising a sensor data collection layer, an AI/ML processing engine layer, and a decision and action output layer, linked by a continuous learning feedback loop. Evidence synthesised from empirical studies and industry benchmarks suggests that AI-PdM adoption has the potential to reduce unplanned downtime by up to 70% and lower maintenance costs by approximately 40% in comparable manufacturing contexts, with positive return on investment projected within three years based on normalised industry cost models; actual outcomes will vary by facility scale, equipment type, and baseline maintenance conditions. The review concludes that agro-food processing industries should prioritise IoT sensor integration, invest in AI-literacy training for maintenance personnel, and engage policy makers to develop supportive agricultural engineering technology frameworks.