Rapid and accurate detection of Camellia oleifera fruit is beneficial to improve the picking efficiency. However, detection faces new challenges because of the complex field environment. A Camellia oleifera fruit detection method based on YOLOv7 network and multiple data augmentation was proposed to detect Camellia oleifera fruit in complex field scenes. Firstly, the images of Camellia oleifera fruit were collected in the field to establish training and test sets. Detection performance was then compared among YOLOv7, YOLOv5s, YOLOv3-spp and Faster R-CNN networks. The YOLOv7 network with the best performance was selected. A DA-YOLOv7 model was established via the YOLOv7 network combined with various data augmentation methods. The DA-YOLOv7 model had the best detection performance and a strong generalisation ability in complex scenes, with mAP, Precision, Recall, F1 score and average detection time of 96.03%, 94.76%, 95.54%, 95.15% and 0.025 s per image, respectively. Therefore, YOLOv7 combined with data augmentation can be used to detect Camellia oleifera fruit in complex scenes. This study provides a theoretical reference for the detection and harvesting of crops under complex conditions.

This study examines the means of reducing the damage to the branches of Camellia oleifera in the process of vibration picking and solving the problems of low equipment-development efficiency and slow product renewal caused by using traditional test methods to determine vibration picking parameters. In this study, the optimal vibration parameters were determined by using the self-response (branch acceleration and strain) law of the Camellia oleifera tree, and finite element analysis and experiments are used to solve this problem. The 3D model of Camellia oleifera was built by Solidworks. The natural frequencies of Camellia oleifera were analyzed by modal analysis, the vibration frequency and amplitude were determined by harmonic response analysis, and transient analysis was used to compare with the test results. The results show that the optimal vibration frequency range of Camellia oleifera is 4~10 Hz, and the average correlation coefficient between the maximum synthetic acceleration and the simulated value is 0.85, which shows that the model can reliably predict the vibration response. At the same time, the best vibration parameters were determined to be 9 Hz, 60 mm and 10 s. Under these parameters, the abscission rate of the Camellia oleifera fruit was 90%, and the damage rate of the flower bud was 13%. The mechanized picking effect of Camellia oleifera fruit was good. This study can quickly determine the vibration picking parameters of Camellia oleifera fruit and effectively improve the development speed of vibration picking of Camellia oleifera fruit.

The key technology of automated apple harvesting is detecting apples quickly and accurately. The traditional detection methods of apple detection are often slow and inaccurate in unstructured orchards. Therefore, this article proposes an improved YOLOv5s-GBR model for orchard apple detection under complex natural conditions. First, the researchers collected photos of apples in their natural environments from different angles; then, we enhanced the dataset by changing the brightness, rotating the images, and adding noise. In the YOLOv5s network, the following modules were introduced to improve its performance: First, the YOLOv5s model’s backbone network was swapped out for the GhostNetV2 module. The goal of this improvement was to lessen the computational burden on the YOLOv5s algorithm while increasing the detection speed. Second, the bi-level routing spatial attention module (BRSAM), which combines spatial attention (SA) with bi-level routing attention (BRA), was used in this study. By strengthening the model’s capacity to extract important characteristics from the target, its generality and robustness were enhanced. Lastly, this research replaced the original bounding box loss function with a repulsion loss function to detect overlapping targets. This model performs better in detection, especially in situations involving occluded and overlapping targets. According to the test results, the YOLOv5s-GBR model improved the average precision by 4.1% and recall by 4.0% compared to those of the original YOLOv5s model, with an impressive detection accuracy of 98.20% at a frame rate of only 101.2 fps. The improved algorithm increases the recognition accuracy by 12.7%, 10.6%, 5.9%, 2.7%, 1.9%, 0.8%, 2.6%, and 5.3% compared to those of YOLOv5-lite-s, YOLOv5-lite-e, yolov4-tiny, YOLOv5m, YOLOv5l, YOLOv8s, Faster R-CNN, and SSD, respectively, and the YOLOv5s-GBR model can be used to accurately recognize overlapping or occluded apples, which can be subsequently deployed in picked robots to meet the realistic demand of real-time apple detection.