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, data-driven, and sustainable agriculture