The sex pheromones of Ectropis grisescens Warren and Ectropis obliqua Prout were both reported to contain (Z,Z,Z)-3,6,9-octadecatriene (Z3,Z6,Z9-18:H) and (Z,Z)-3,9-cis-6,7-epoxy-octadecadiene (Z3,epo6,Z9-18:H). To clarify how these two sibling geometrids maintain premating isolation, the female sex pheromones of the two species were reexamined. Gas chromatography–electroantennographic detection (GC-EAD) and gas chromatography–mass spectrometry revealed two GC-EAD-active compounds, Z3,Z6,Z9-18:H and Z3,epo6,Z9-18:H, in E. grisescens female pheromone glands as well as an additional GC-EAD-active compound, (Z,Z)-3,9-cis-6,7-epoxy-nonadecadiene (Z3,epo6,Z9-19:H), in E. obliqua female pheromone glands. Synthesized Z3,Z6,Z9-18:H and Z3,epo6,Z9-18:H elicited dose-dependent electroantennogram (EAG) responses from male antennae of both E. grisescens and E. obliqua. However, Z3,epo6,Z9-19:H only elicited dose-dependent EAG responses from E. obliqua and limited EAG responses from E. grisescens at all doses. In wind-tunnel studies, lures that contained Z3,Z6,Z9-18:H and Z3,epo6,Z9-18:H attracted E. grisescens males and had no effect on E. obliqua males. The addition of Z3,epo6,Z9-19:H to the blend of Z3,Z6,Z9-18:H and Z3,epo6,Z9-18:H strongly attracted E. obliqua males but had a limited attraction for E. grisescens males. Thus, Z3,Z6,Z9-18:H and Z3,epo6,Z9-18:H were sex pheromone components of E. grisescens, whereas Z3,Z6,Z9-18:H, Z3,epo6,Z9-18:H and Z3,epo6,Z9-19:H were sex pheromone components of E. obliqua. The presence or absence of Z3,epo6,Z9-19:H played a central role in the premating isolation of these two sibling species.

The precise detection and positioning of tea buds are among the major issues in tea picking automation. In this study, a novel algorithm for detecting tea buds and estimating their poses in a field environment was proposed by using a depth camera. This algorithm introduces some improvements to the YOLOv5l architecture. A Coordinate Attention Mechanism (CAM) was inserted into the neck part to accurately position the elements of interest, a BiFPN was used to enhance the small object detection ability, and a GhostConv module replaced the original Conv module in the backbone to reduce the model size and speed up model inference. After testing, the proposed detection model achieved an mAP of 85.2%, a speed of 87.71 FPS, a parameter number of 29.25 M, and a FLOPs value of 59.8 G, which are all better than those achieved with the original model. Next, an optimal pose-vertices search method (OPVSM) was developed to estimate the pose of tea by constructing a graph model to fit the pointcloud. This method could accurately estimate the poses of tea buds, with an overall accuracy of 90%, and it was more flexible and adaptive to the variations in tea buds in terms of size, color, and shape features. Additionally, the experiments demonstrated that the OPVSM could correctly establish the pose of tea buds through pointcloud downsampling by using voxel filtering with a 2 mm × 2 mm × 1 mm grid, and this process could effectively reduce the size of the pointcloud to smaller than 800 to ensure that the algorithm could be run within 0.2 s. The results demonstrate the effectiveness of the proposed algorithm for tea bud detection and pose estimation in a field setting. Furthermore, the proposed algorithm has the potential to be used in tea picking robots and also can be extended to other crops and objects, making it a valuable tool for precision agriculture and robotic applications.

Rose tea is a type of flower tea in China’s reprocessed tea category, which is divided into seven grades, including super flower, primary flower, flower bud, flower heart, yellow flower, scattered flower, and waste flower. Grading rose tea into distinct quality levels is a practice that is essential to boosting their competitive advantage. Manual grading is inefficient. We provide a lightweight model to advance rose tea grading automation. Firstly, four kinds of attention mechanisms were introduced into the backbone and compared. According to the experimental results, the Convolutional Block Attention Module (CBAM) was chosen in the end due to its ultimate capacity to enhance the overall detection performance of the model. Second, the lightweight module C2fGhost was utilized to change the original C2f module in the neck to lighten the network while maintaining detection performance. Finally, we used the SIoU loss in place of the CIoU loss to improve the boundary regression performance of the model. The results showed that the mAP, precision (P), recall (R), FPS, GFLOPs, and Params values of the proposed model were 86.16%, 89.77%, 83.01%, 166.58, 7.978, and 2.746 M, respectively. Compared with the original model, the mAP, P, and R values increased by 0.67%, 0.73%, and 0.64%, the GFLOPs and Params decreased by 0.88 and 0.411 M, respectively, and the speed was comparable. The model proposed in this study also performed better than other advanced detection models. It provides theoretical research and technical support for the intelligent grading of roses.