Zhengjun Li ^1* Yijie Wu ^1* Haoyu Jiang ^1* Deyi Lei ^1* Feng Pan ^2* Jinxin Qiao ^1* Xiuqing Fu ^1* Biao Guo ^1*

• ¹ Nanjing Agricultural University, Nanjing, China
• ² Xinjiang Academy of Agricultural and Reclamation Sciences (XAARS), Shihezi, Xinjiang Uyghur Region, China

Existing seed germination detection technologies based on deep learning are typically optimized for hydroponic breeding environments, leading to a decrease in recognition accuracy in complex soil cultivation environments. On the other hand, traditional manual germination detection methods are associated with high labor costs, long processing times, and high error rates, with these issues becoming more pronounced in complex soil-based environments. To address these issues in the germination process of new cucumber varieties, this paper utilized a Seed Germination Phenotyping System to construct a cucumber germination soil-based experimental environment that is more closely aligned with actual production. This system captures images of cucumber germination under salt stress in a soil-based environment, constructs a cucumber germination dataset, and designs a lightweight real-time cucumber germination detection model based on RT-DETR. By introducing online image enhancement, incorporating the Adown downsampling operator, replacing the backbone convolutional block with Generalized Efficient Lightweight Network (GLEAN) , introducing the Online Convolutional Re-parameterization (OREPA) mechanism, and adding the Normalized Gaussian Wasserstein Distance (NWD) loss function, the training effectiveness of the model is enhanced. This enhances the model's capability to capture profound semantic details, achieves significant lightweighting, and enhances the model's capability to capture embryonic root targets, ultimately completing the construction of the RT-DETR-SoilCuc model.The results show that compared to the RT-DETR-R18 model, the RT-DETR-SoilCuc model exhibits a 61.2% reduction in Params, 61% reduction in FLOP, and 56.5% reduction in Weight Size. Its mAP@0.5, Precision, and Recall are 98.2%, 97.4%, and 96.9% respectively, demonstrating certain advantages over the You Only Look Once (YOLO) series models of similar size. Germination tests of cucumbers under different concentrations of salt stress in a soil-based environment were conducted, validating the high accuracy of the RT-DETR-SoilCuc model for embryonic root target detection in the presence of soil background interference. This research reduces the manual workload in the monitoring of cucumber germination and provides a method for the selection and breeding of new cucumber varieties.