SSP-MambaNet: An automated system for detection and counting of missing seedlings in glass greenhouse-grown virus-free strawberry

Precisely identifying missing virus-free strawberry mother plants in nutrient pots post-transplantation is crucial for optimizing seedling management and maximizing yields in glass greenhouses. Thus, we present an automated method for detecting and counting missing seedlings based on SSP-MambaNet. Challenges in this process include the variable growth morphology of seedlings and complex environmental conditions in the greenhouse. Our approach starts with SPDFFA (Spatial-to-Depth Feature Fusion Attention) to enhance feature representation while retaining critical information, ensuring the preservation of key details. Additionally, the multi-scale CVSSB(Complex Visual State Space) and CVSSB-E(Expanded CVSSB) modules combine multi-scale and multi-directional spatial features, augmenting the model's capacity to recognize inter-image dependencies. Secondly, the MPDIoU is a novel loss function to tackle the optimization challenge of bounding boxes with similar shapes but different sizes, which enhances the accuracy of localizing strawberry seedlings and nutrient pots. Finally, Distance Intersection over Union is utilized for establishing a belongingness relationship between strawberry seedlings and pots, accurately identifying missing seedlings and counting the corresponding pots. Experimental results demonstrate that SSP-MambaNet achieves 94.9 %in average precision, 92.8 ​% in recall rate,88.1 ​% in precision, and 90.4 ​% F1 score for strawberry seedlings and pots. It outperforms the YOLOv7 by 4.7 ​% in average precision, and 2.6 ​% in recall rate while reducing 66.7 f/s in FPS. Furthermore, the proposed method shows 94.29 ​% accuracy in detecting missing seedlings and 97.14 ​% accuracy in counting nutrient pots with missing seedlings. These results showcase its effectiveness in improving overall seedling quality and providing timely replanting guidance in glass greenhouses.