TOWARDS EXPLAINABLE AI FOR EARLY DETECTION AND PREDICTION OF FAILURES IN SMART AGRICULTURE

Abstract

Smart agriculture is rapidly evolving with the integration of advanced technologies such as the Internet of Things (IoT), Machine Learning (ML), and Artificial Intelligence (AI) to improve productivity and operational efficiency. However, one of the key challenges in deploying AI-based predictive systems is the lack of transparency and interpretability, often referred to as the “black-box” problem. This limitation reduces user trust and hinders effective decision-making, especially in critical domains like agriculture. This project proposes an Explainable Artificial Intelligence (XAI)-based predictive maintenance system for early detection and prediction of equipment failures in smart agricultural environments. The system collects real-time sensor data, including parameters such as temperature, humidity, vibration, and pressure, from IoT-enabled agricultural devices. This data is preprocessed and analyzed using machine learning and deep learning models such as Random Forest, Naive Bayes, and Neural Networks to identify failure patterns and predict potential breakdowns in advance. To enhance transparency and interpretability, explainability techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) are integrated into the system. These methods provide clear insights into model predictions by highlighting the contribution of each feature, enabling farmers and technicians to understand the reasons behind predicted failures. The system also includes a user-friendly dashboard for real-time monitoring, visualization, and actionable suggestions. Experimental results demonstrate high prediction accuracy, improved reliability, and efficient handling of large-scale sensor data. By enabling early fault detection and providing interpretable insights, the proposed system reduces maintenance costs, minimizes downtime, and supports proactive decision-making. Overall, this approach contributes to the development of sustainable, transparent, and intelligent smart agricultural systems