Efficient and timely monitoring of plant diseases in large-scale farms is critical for maintaining crop health and minimizing economic losses. Current manual inspection and static image-based methods are limited by scalability and real-time detection capabilities. This paper presents a conceptual framework for a UAV-based multi-sensor fusion system aimed at enhancing plant disease detection in large-scale agricultural environments. The system integrates aerial imaging and environmental sensor data (e.g., temperature, humidity, light) using multi-modal data fusion techniques to improve the accuracy of disease prediction. The proposed architecture also leverages optimized flight path planning to ensure comprehensive coverage of the monitoring area. The framework is evaluated using theoretical analysis and existing data from plant disease datasets, and simulated sensor data is employed to demonstrate the potential improvements in disease detection efficiency. This design framework sets the stage for future development and deployment of UAV-based smart farming systems, offering improved scalability, real-time response, and accurate plant disease monitoring.