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

Abstract

Smart agriculture uses modern technologies like IoT, sensors, and Artificial Intelligence (AI) to improve farming efficiency and productivity. However, one of the major challenges in using AI systems is the lack of explainability, which makes it difficult for farmers to trust and understand the model’s predictions. This project focuses on developing an Explainable Artificial Intelligence (XAI) system for the early detection and prediction of equipment failures in smart farms. The system collects real-time data from IoT sensors such as temperature, humidity, and vibration, and analyses it using Machine Learning and Deep Learning models. These models predict possible failures before they happen, helping farmers take preventive actions in time. The explainable AI techniques, such as SHAP and LIME, are used to make the model’s predictions easy to interpret and understand. This ensures transparency, trust, and better decision-making for farmers. Overall, this approach reduces maintenance costs, prevents unexpected breakdowns, and promotes smart, datadriven, and sustainable agriculture This project proposes an Explainable AI (XAI)-based system for early detection and prediction of failures in smart agriculture. Unlike traditional AI models that act as “black boxes,” XAI provides transparency by explaining the reasons behind predictions. This helps farmers and stakeholders understand system behavior and take appropriate actions. The system collects data from sensors such as soil moisture, temperature, humidity, and weather conditions. Machine learning models analyze this data to predict potential failures such as irrigation system faults, crop diseases, or abnormal environmental conditions. Explainability techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Modelagnostic Explanations) are used to provide insights into the model’s decisions. The proposed system improves reliability, trust, and decision-making in smart agriculture. It enables proactive actions, reduces risks, and enhances sustainability. The system can be deployed in farms as a real-time monitoring tool integrated with mobile or web applications.