Smart Agriculture System for Pest Detection and Drought Prediction using AI

This research presents an AI-based smart agricultural monitoring and prediction framework designed for pest detection and drought forecasting using Deep Learning, Internet of Things (IoT), Remote Sensing, and Explainable Artificial Intelligence (XAI). The proposed framework integrates IoT-enabled sensors, satellite imagery, environmental monitoring systems, and intelligent machine learning models such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Long Short-Term Memory (LSTM) networks, and Random Forest algorithms for real-time agricultural analysis. The system aims to improve crop productivity, optimize water resource management, reduce pesticide overuse, and support sustainable farming practices through intelligent prediction and monitoring techniques. This work represents a conceptual research framework, and future implementation and experimental validation are planned for practical agricultural deployment.