YOLOv11n-KL: A Lightweight Tomato Pest and Disease Detection Model for Edge Devices

Frequent occurrences of pests and diseases in tomatoes severely restrict yield and quality improvements. Traditional detection methods are labor-intensive and prone to errors, while advancements in deep learning provide a promising solution for rapid and accurate identification. However, existing deep learning-based models often face high computational complexity and a large number of parameters, which hinder their deployment on resource-constrained edge devices. To overcome this limitation, we propose a novel lightweight detection model named YOLOv11n-KL based on the YOLOv11n framework. In this model, the feature extraction capability for small targets and the overall computational efficiency are enhanced through the integration of the Conv_KW and C3k2_KW modules, both of which incorporate the KernelWarehouse (KW) algorithm, and the Detect_LSCD detection head is employed to enable parameter sharing and adaptive multi-scale feature calibration. The results indicate that YOLOv11n-KL achieves superior performance in tomato pest and disease detection, balancing lightweight design and detection accuracy. The model achieves an [email protected] of 92.5% with only 3.0 GFLOPs and 5.2 M parameters, reducing computational cost by 52.4% and improving [email protected] by 0.9% over YOLOv11n. With its low complexity and high precision, YOLOv11n-KL is well-suited for resource-constrained applications. The proposed YOLOv11n-KL model offers an effective solution for detecting tomato pests and diseases, serving as a useful reference for agricultural automation.