Explainable Artificial Intelligence Model For Predictive Maintenance In Agricultural Facilities

Abstract

Predictive maintenance has become an essential approach for improving the reliability and
operational efficiency of agricultural machinery and facilities. Traditional maintenance strategies
such as reactive and preventive maintenance often result in unexpected equipment failures, increased
downtime, and higher operational costs. This study proposes an Explainable Artificial Intelligence
(XAI) based predictive maintenance system implemented using a Django web framework integrated
with machine learning models. The system enables real-time monitoring, data preprocessing, model
training, and failure prediction through an interactive web interface. The proposed model utilizes
machine learning algorithms such as Logistic Regression, Support Vector Machine, and Random
Forest to analyze equipment operational parameters including temperature, rotational speed, torque,
and tool wear to predict potential machine failures. The dataset is preprocessed through
normalization, encoding, and train–test splitting to improve model performance. The system
automatically selects the best-performing model based on accuracy and provides visual performance
comparisons using graphical analysis. Additionally, the predictive model allows users to input
machine parameters and obtain failure predictions along with probability scores, enhancing
interpretability and decision support. By incorporating explainable AI concepts, the system improves
transparency in machine learning predictions, allowing agricultural operators and technicians to
better understand the factors contributing to equipment failure. The proposed approach supports
proactive maintenance planning, reduces downtime, and improves productivity in agricultural
facilities. The implementation demonstrates how web-based machine learning systems can be
effectively utilized for intelligent predictive maintenance in smart agriculture environments