<p>Precision farming enables farmers to make informed decisions regarding fertilization, irrigation, and harvesting by leveraging IoT-enabled sensors that collect real-time data on moisture, temperature, soil nutrients, and other environmental factors. Wireless Sensor Networks (WSNs) in agriculture face challenges such as high energy consumption, security vulnerabilities, and limited real-time data processing capabilities. To address these issues, this paper proposes an Improved Weighted Quantum Whale Optimization (IWQWO) integrated with a Vision Transformer (ViT) for secure and efficient environmental monitoring and intrusion detection in smart agriculture. The IWQWO algorithm combines quantum-inspired techniques with adaptive weighting to optimize node clustering, routing efficiency, and anomaly detection, enhancing energy efficiency and system security. Concurrently, the Vision Transformer captures spatial-temporal relationships in sensor data, ensuring high-precision monitoring, improved intrusion detection, and reduced false alarms. The framework also facilitates resource management, supply-demand prediction, and integration of modern IoT technologies with traditional agricultural practices, including automated irrigation, drone-assisted monitoring, and plant disease detection. Extensive evaluations demonstrate that the proposed IWQWO-ViT model surpasses existing approaches in detection accuracy, cost-effectiveness, and network reliability, offering a robust solution for intelligent, secure, and sustainable agricultural automation.</p>