Stem and leaf obstruction, interlacing, and overlapping are common phenomena during the growth process of field-grown strawberries, posing significant challenges for fruit target detection by harvesting robots. To address target detection failures caused by foliage occlusion in strawberry cultivation, a synergistic algorithm integrating instance segmentation with occlusion detection, occlusion prediction, and airflow-optimized deoccultation was proposed. Firstly, a real-time segmentation model based on YOLO 11 seg was constructed to generate complete masks for strawberry fruits and occluding objects in complex scenes, followed by analysis to determine the current strawberry occlusion rate. For non-severely occluded regions (current occlusion rate no more than 70% ), the system directly targeted the geometric center of the strawberry mask for air-blowing intervention without initiating the spiral search-region growing algorithm. For heavily obstructed target areas (current obstruction rate greater than 70% ), a spiral search region growing model search algorithm was developed to locate the optimal airblowing intervention zone, precisely capturing the temporal characteristics of obstruction rate evolution. A lightweight CNN then used spiral features as input to accurately predict the post-air-blowing obstruction rate. Finally, a multi-parameter adjustable air-blowing device physically removed obstructions through integrated operation. Regarding occlusion rate prediction, the method achieved high accuracy in occlusion information forecasting (R2 was 0.925, RMSE was 2.57%), significantly enhancing estimation accuracy and adaptability to complex environments. Through the implementation of the integrated approach, i. e. , encompassing detection, prediction, and air-blowing removal, the method underwent field trials. Results demonstrated its effectiveness in reducing strawberry fruit occlusion rates under stemand-leaf shading scenarios, validating the algorithm's efficacy and providing a “ detection-predictionremoval” solution for crop de-occlusion in protected agriculture. Field trial results indicated that the system reduced the average occlusion rate from 68.5% to 12.8% across 90 severely occluded samples. In 82 samples (91.1% ), the occlusion rate fell below 15% , significantly enhancing the robustness of ground-grown strawberry identification and robotic harvesting adaptability in complex environments.