In ridge-cultivated strawberry environments, significant scale variation between fruits and stems, severe occlusion, and high missed-detection rates for small or slender targets pose major challenges to accurate picking point localization. This study proposes a fruit-stem oriented detection and picking point localization method based on an improved YOLOv11n-OBB model. Specifically, an enhanced model termed FSGE-OBB is developed. A Feature Fusion Lite Module (FFLM) is introduced to strengthen cross-layer interaction between shallow spatial details and deep semantic features, thereby improving multi-scale feature representation. A Stem Direction Enhancement Module (SDEM) is designed to enhance the directional perception of slender stems. In addition, Grouped Spatial Excitation Convolution (GSEConv) is adopted to reduce model complexity while maintaining feature representation capability, and an Efficient Upsampling Convolution Block (EUCB) is incorporated into high-resolution feature maps to improve detail recovery. Based on the oriented detection results, a geometry-constrained fruit-stem association method is established by modeling the spatial relationship between fruit and stem oriented bounding boxes (OBBs), and the corresponding picking points are localized accordingly. Experimental results show that the FSGE-OBB model achieves mAP@0.5 and mAP@0.5:0.95 of 78.85% and 68.27%, respectively, outperforming the YOLOv11n-OBB baseline by 2.59 and 1.90 percentage points. The proposed fruit-stem association method achieves a success rate of 97.99%. In preliminary three dimensional localization experiments, the mean absolute errors of picking points are 2.2 mm, 2.6 mm, and 2.3 mm in the x- , y- , and z- directions, respectively. Furthermore, simulated harvesting experiments achieve a success rate of 93.3%, with an average harvesting time of 9.2 s per fruit, validating the feasibility and effectiveness of the proposed method in practical harvesting tasks. These results demonstrate that the proposed FSGE-OBB-based framework enables unified modeling of fruit-stem detection, association, and picking point localization, providing a feasible visual perception solution for strawberry harvesting robots.

Citrus, as a globally important economic crop, requires accurate assessment of premium fruit production potential for precise orchard management and enhanced economic benefits. This study develops a method for assessing the production potential of premium citrus using UAV-based multispectral imagery and ground data. Taking citrus orchards in Wuming District, Guangxi, China, as the experimental area, this study investigates techniques for assessing the production potential of premium fruit at the canopy scale of citrus trees in southern hilly regions, aiming to rapidly predict the quality production potential of citrus before fruit ripening. The methodology involved the following: (1) Segmenting the study area using a Digital Surface Model (DSM) and extracting individual tree canopies by integrating NDVI with a marker-controlled watershed algorithm. Canopy fruit boundaries were identified using the NPCI index. (2) Selecting key assessment indicators—NDVI, TCAVI, REOSAVI, canopy area, and canopy fruit area—through correlation analysis with nutritional quality metrics. (3) Establishing threshold levels for these indicators and constructing a production potential assessment model. Experimental results demonstrated an individual tree identification accuracy (precision) of 98.75%, a recall of 98.47%, and an F-score of 98.61%. Canopy area extraction achieved a coefficient of determination (R²) of 0.869 and a root mean square error (RMSE) of 0.489 m². The overall accuracy for production potential assessment reached 85.11%. This study provides a new approach for using UAV multispectral technology to non-destructively assess the production potential of premium citrus in the hilly regions of southern China, offering technical support for precise orchard management.