To address the issues of a low single-seed qualification index and a high missed-seeding index in the process of leafy vegetable plug seedling sowing, this study proposes a lightweight seeding performance detection model named VS-YOLO based on YOLO11n. The model is then deployed on the edge device, the NVIDIA Jetson Xavier NX. A concise and intuitive graphical user interface (GUI) was developed and an automated detection system for vegetable seeding performance was constructed. Based on the empty cells identified by the system, a real-time data transmission mechanism between the Jetson device and a PLC-based control unit is established, enabling the intelligent reseeding device to perform precise reseeding at the designated cell location, achieving row-wise and cell-specific intelligent planting. VS-YOLO incorporates several innovative improvements, including the introduction of a Context Anchor Attention (CAA) module to form the C2PSACAA module, the adoption of the Wise Intersection over Union version 3 (WIoU v3) loss function, and the addition of an extra-small object detection head. These enhancements significantly improve the classification and recognition capability for small-sized vegetable seeds while notably reducing the number of model parameters. Experimental results show that VS-YOLO achieves a mAP@0. 5 of 96. 5% and an F1 Score of 93. 45% in detecting the seeding performance of three types of vegetable seeds, outperforming YOLO11n’s 91. 5% and 85. 19% by 5. 0% and 8. 26%. The parameter count of VS-YOLO is only 1. 61 M, which is 37. 6% lower than YOLO11n’s 2. 58 M, making it lightweight. Operating at a productivity rate of 120 trays per hour, the system achieved an accuracy of 99. 03%, 89. 83%, and 92. 26% for single-seed prediction, multiple-seeding prediction, and missed-seeding prediction. The single-seed qualification index and missed-seeding index were 93. 43% and 4. 68%. After reseeding, these indices improved to 97. 61% and 0. 32%, representing an increase of 4. 18% in the single-seed qualification index and a decrease of 4. 36% in the missed-seeding index. The significant enhancement offers new ideas and technical approaches for the advancement of seeding performance detection and reseeding systems for vegetable plug seedling production.
Zhong et al. (Thu,) studied this question.