For efficient performance of selective harvesting robots, accurate target recognition and locating are the prerequisite and foundation. Therefore, the adaptability of fruit target recognition algorithms, both for the multiple fruit species and for the embedded edge computing platforms, has the potential to expand the application scope of picking robots. Since the citrus fruits have diverse morphology and a wide distribution in orchards, a lightweight citrus fruit recognition model based on improved YOLOv8 was designed to recognize the oranges ( Citrus sinensis ), tangerines ( Citrus reticulata ) and pomelos ( Citrus maxima ) fruit on trees with less computation and tested on a picking robot arm by integrating with manipulator control programs. To improve the adaptability, the backbone network was replaced with lightweight ShuffleNetV2, a LightWeight Head detection head based on the concept of shared convolutional layers was constructed and the feature pyramid network (FPN) was redesigned into which SEAttention (Squeeze-Excitation Attention) was incorporated while integrating the advantages of MPDIoU and Focaler-IoU to construct an optimized CIoU loss function. With 7:1:2 as the ratio of training, validation and test sets, 2,250 images of orange, tangerine and pomelo fruit on the tree were involved in the training and test of the improved model. The weighted-average recognition precision, recall and mean average precision (mAP) of the test set were 92.5%, 82.0% and 89.4% for three species while the mAP values for independent orange, tangerine and pomelo test sets were 92.6%, 86.9% and 88.7%, respectively. Compared with the original YOLOv8 model, the improved model reduced the computational load by 58%, with the inference speed reaching 15.35 FPS on a workstation CPU (an improvement of 45.9%) and 0.76 FPS on a Raspberry Pi 4B (an improvement of 38.2%). The localization experiment results showed that, in the manipulator base coordinate system, the average errors of the predicted center coordinates for oranges, tangerines and pomelos were within 10 mm in the X, Y and Z directions and the radius errors under different postures were all within 6 mm. To verify the practicality of the improved algorithm, 312 fruit grasping tests were conducted in orchards. The results showed that, for oranges, tangerines and pomelos, the recognition success rates were 93.5%, 93.6% and 88.3%, the localization success rates were 90.2%, 88.1% and 83.8% and the grasping success rates were 88.0%, 76.1% and 68.5%, respectively. The proposed YOLOv8n-Light model has provided a technical basis for citrus fruit picking operations and references for the design and improvement of fruit recognition algorithms for diverse fruit species. • Multiple citrus varieties (oranges, tangerines, pomelos) were covered with high recognition success rates. • The improved model achieved lightweight design and was convenient for deployment on low-computing embedded platforms. • The improved algorithm demonstrated excellent practicality in orchard tests.
Ma et al. (Wed,) studied this question.