Key Features and Innovations: 

• Instance Segmentation for Small Pest Detection: 
  Unlike traditional object detection methods that use bounding boxes, our system employs instance segmentation to achieve pixel-level accuracy. This allows precise identification and counting of SWD, even in complex agricultural environments. 

• Edge Computing for Autonomous Operation: 
  The system integrates a Raspberry Pi with solar-powered hardware to enable real-time image processing directly in the field, eliminating the need for constant cloud connectivity. This significantly reduces bandwidth usage and ensures reliability in remote areas. 

• High-Resolution Imaging and Robust Design: 
  Equipped with a 16-megapixel Arducam camera and housed in an IP65 weatherproof enclosure, the system operates seamlessly under extreme environmental conditions. It captures high-resolution images at regular intervals, ensuring detailed monitoring of SWD activity throughout the growing season. 

• Sustainable and Scalable Deployment: 
  Powered entirely by solar energy and featuring efficient power management, the system is designed for long-term, autonomous operation across diverse agricultural landscapes. Its modular design allows for easy adaptation to various crop types and monitoring locations. 

Research Challenges Addressed: 
Traditional pest monitoring relies on manual techniques, such as sticky traps and visual inspections, which are labor-intensive and lack scalability. Our approach overcomes these limitations by: 

• Utilizing AI-Driven Models: The use of YOLO-based segmentation models enhances detection accuracy for small pests like SWD, addressing challenges posed by their size and complex backgrounds. 

• Adapting to Field Conditions: The system is optimized for real-world agricultural settings, ensuring consistent performance despite variations in light, temperature, and humidity. 

Impact and Applications: 
This novel monitoring system empowers farmers and researchers with actionable insights for pest management, reducing crop losses and improving yield quality. By integrating advanced AI with robust field hardware, we offer a practical, scalable solution for precision agriculture that supports sustainability and efficiency.