Cyclic nucleotide-gated ion channel (CNGC) genes play vital roles in plant growth, development, and responses to both biotic and abiotic stresses. However, the current research on CNGCs in potato (Solanum tuberosum) remain largely uncharacterized. Blackleg disease is one of the most devastating diseases worldwide, causing severe yield losses. Understanding the role of the StCNGC gene family in blackleg resistance is therefore of significant importance. In this study, we identified 11 StCNGC genes in the potato genome and conducted phylogenetic analysis, gene structure characterization, and conserved motif prediction. Expression patterns were examined in different tissues and under stress conditions. The identified StCNGCs were classified into five groups, and showed conserved gene structures and motifs within groups. Most StCNGCs were induced under biotic stress conditions. Notably, silencing StCNGC2 conferred resistance to blackleg disease and resulted in the upregulation the pathogenesis-related marker gene StPR1. Together, these findings suggest that StCNGC2 plays a crucial role in potato defense against blackleg disease and provide a foundation for further functional studies of the StCNGC gene family.

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 mAP@0.5 of 92.5% with only 3.0 GFLOPs and 5.2 M parameters, reducing computational cost by 52.4% and improving mAP@0.5 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.