YOLOP-Tomato: An End-to-End Model for Tomato Detection and Main Stem–Lateral Branch Segmentation

Tomatoes are a rich source of nutrients that are essential for human health. However, in greenhouse environments, the complex growth patterns of tomatoes and stems often result in mutual obstruction and overlapping, posing significant challenges for accurate ripeness detection and stem segmentation. Furthermore, the current detection and segmentation tasks are typically executed in isolation, resulting in suboptimal inference efficiency and substantial computational expenses. To address these issues, this study proposes the YOLOP-Tomato (YOLO-Based Panoptic Perception for Tomato) based on YOLOv8n, enabling simultaneous tomato detection and stem and branch segmentation. Two RSU (ReSidual U-blocks) modules establish feature connection mechanisms between the backbone and head. SPPCTX (SPP Context) was developed at the neck of the model to perform multi-scale contextual feature fusion and enhancement. The SCDown (Spatial-Channel Decoupled downsampling) is employed to lightweight the backbone’s terminal structure. The experimental results demonstrate that YOLOP-Tomato achieves precision, recall, mAP50, and mAP50–95 of 94.9%, 85.0%, 93.6%, and 60.9% for detection, and mIoU of 77.6% for segmentation. These results represent improvements of 2.5%, 0.1%, 0.5%, 1.1%, and 1.4%, over YOLOv8n. The trained model was deployed on the NVIDIA Jetson AGX Orin platform, an efficient inference speed of 5.67 milliseconds was achieved. The proposed YOLOP-Tomato provides reliable and efficient technical support for tomato detection, ripeness identification, stem and branch segmentation in greenhouses, and holds great significance for improving the level of intelligent agricultural production.