Enhancing integrated pest management with IoT and YOLO-Evo: A smart, low-cost monitoring system for sustainable apple farming

• Developed a smart insect trap integrating YOLO-Evo with IoT technology. • Proposed a cost-effective alternative to expensive commercial electronic traps. • Demonstrated the impact of dataset diversity on detection accuracy in traps. • Enhanced sustainable farming with real-time, eco-friendly pest control practices. • Supports Integrated Pest Management through precise, targeted detection. Effective pest monitoring is critical for reducing crop losses in apple orchards, especially against codling moths (Cydia pomonella). Traditional inspection methods are labor-intensive and lack real-time responsiveness, prompting the need for automated solutions. This study proposes a smart, low-cost pest detection system that integrates deep learning and IoT technologies. The core of the system is the YOLO-Evo model—an improved variant of YOLOv10—deployed on a solar-powered smart trap equipped with a Raspberry Pi 4B. It performs on-device insect detection and transmits real-time data to the ThingsBoard IoT platform via cellular communication. The model was trained on a dataset of 1,011 images containing 2,707 annotated insect instances, and validated using an 85:15 train-validation split. Comparative experiments with six YOLO variants (YOLOv5–YOLOv11) demonstrate that YOLO-Evo achieves a superior mAP50–95 of 98.11%, representing an 8.75% gain over YOLOv10-m (89.36%), while maintaining high confidence scores (86–95%) and low computational cost (27.8 GFLOPs). The system also supports automated alerts and geolocated trap monitoring through a mobile dashboard and app. A cost analysis reveals that the proposed system, priced under $300, is significantly more affordable than commercial electronic traps ($850–$1000). Beyond detection accuracy, this work promotes sustainable agriculture by enabling timely pest control, reducing pesticide overuse, and facilitating scalable deployment. The system’s design supports adaptation to other crops, ensuring long-term versatility. This research directly aligns with Integrated Pest Management (IPM) principles by improving decision-making, reducing environmental impact, and enhancing precision in pest control strategies.