The fruit and vegetable classification problem is an inseparable branch in the field of image recognition. GoogLeNet provides a more ideal solution for the fruit and vegetable classification problem. We use the GoogLeNet network to classify apples, lemons, oranges, pomegranates, tomatoes, and colored peppers. Through experiments, we obtained the training accuracy of GoogLeNet as 96.88%, the testing accuracy as 96%, and the training speed as 11.38 sheets/second. The recognition accuracy of this model can meet the basic recognition requirements, but the training speed is low. Therefore, we decided to optimize GoogLeNet to significantly improve the training speed and further enhance the recognition accuracy of GoogLeNet. We reduced the number of convolutional kernels of GoogLeNet and adjusted the structure of Inception, which reduced the number of parameters of GoogLeNet by nearly 48% and increased the training speed of GoogLeNet from 11.38 to 33.68 sheets/second. To further improve its recognition accuracy, we tried two methods: 1) introducing a new activation function Swish; 2) between convolutional layers, we introduced DropBlock layer; these two methods improved the testing accuracy of GoogLeNet by 2%. Finally, we introduce AlexNet, VGGNet, ResNet18, DenseNet121, and Inception-ResNet to compare with our optimized GoogLeNet. By comparison, we found that our model has incredible performance in ACC, AUC, FPS, Recall, etc.

In response to the navigation requirements of robots in orchard environments, this paper presents a navigation method for orchard robots based on point cloud maps. First, point cloud maps are processed with pass-through filtering and PCA algorithms to make them suitable for path planning. Next, tree rows within the orchard are clustered and segmented based on map orientations. Then, the least squares method and the $\text{A}^{\ast} $ algorithm are combined for global path planning on local maps. Lastly, the TEB local path planning algorithm is employed to ensure that the robot navigates along the operation path. Experimental results indicate that the robot can successfully navigate orchards at speeds ranging from 0.4 to 1.0 m/s. The average longitudinal deviation obtained under these conditions is 26.7 cm, with a maximum value not exceeding 46.2 cm. The average heading angle deviation is 4.09°, with a maximum value of 8.65°. In conclusion, this approach ensures comprehensive navigation for orchard robots and lays a strong foundation for future research.

Accurate fruit tree models are essential for canopy volume measurement work, we build an orchard mobile robot platform and develop a fruit tree model reconstruction algorithm based on it, optimize the LIDAR Odometry specifically for the orchard environment, fuse the LIDAR Odometry, Inertial Measurement Unit (IMU), Global Navigation Satellite System (GNSS) sensor information and loop closure detection in the form of factors to add factor maps for back-end optimization to reconstruct the orchard map model, use the sliding window method to process in real time The fused information and narrowed the range where the tree trunks are located for two times of line surface feature matching, and the point cloud data are processed to get the fruit tree model. In order to make the point cloud distribution of the reconstructed model uniform, the robot also needs to match a specific walking route, and use the Hough transform and K-Means clustering algorithm to extract the linear-circular-linear walking route autonomously according to the tree row arrangement. The experimental results show that the error of the map model is less than 0.160 m, and the correlation coefficient R ² and root-mean-square error (RMSE) of the canopy model for volume measurement are 0.984 and 0.102 m ³ , respectively. The collected LiDAR data based on the robotic platform meets the requirement of fruit canopy volume calculation.

The efficient detection of grapes is a crucial technology for fruit-picking robots. To better identify grapes from branch shading that is similar to the fruit color and improve the detection accuracy of green grapes due to cluster adhesion, this study proposes a Shine-Muscat Grape Detection Model (S-MGDM) based on improved YOLOv3 for the ripening stage. DenseNet is fused in the backbone feature extraction network to extract richer underlying grape information; depth-separable convolution, CBAM, and SPPNet are added in the multi-scale detection module to increase the perceptual field of grape targets and reduce the model computation; meanwhile, PANet is combined with FPN to promote inter-network information flow and iteratively extract grape features. In addition, the CIOU regression loss function is used and the prior frame size is modified by the k-means algorithm to improve the accuracy of detection. The improved detection model achieves an AP value of 96.73% and an F1 value of 91% on the test set, which are 3.87% and 3% higher than the original network model, respectively; the average detection speed under GPU reaches 26.95 frames/s, which is 6.49 frames/s higher than the original model. The comparison results with several mainstream detection algorithms such as SSD and YOLO series show that the method has excellent detection accuracy and good real-time performance, which is an important reference value for the problem of accurate identification of Shine-Muscat grapes at maturity.

In order to solve the problems of domestic and foreign vegetable precision metering devices that easily damage seeds and have low seeding efficiency and poor qualification rate, this paper designs a one-machine for two-row pneumatic combination seeder, which is mainly composed of a pneumatic seeding mechanism and a honeycomb seeding mechanism. The mechanism and other components and theoretical analysis of the working process of the combined seed metering device clarifies the minimum negative pressure for adsorbing a seed during the suction stage, along with the negative pressure required for the seed-carrying stage. It also establishes the seeding speed and movement during planting with the trajectory parameter equation and uses ADAMS software to conduct virtual experiments on the influence of the number of holes, diameters, and heights of different types of honeycomb seeding disks on the success rate of seeding. Based on research on previous pneumatic seeding mechanisms, single factor tests, orthogonal tests, and high-speed photography tests of the honeycomb seeding disk of the combined seeder were carried out on the number of holes, the diameter of holes, and the height of seeding. The results of tests conducted on okra seeds showed that when the rotating speed of the pneumatic seeding disc was 18 r/min, the diameter of the pneumatic seeding disc hole was 2.4 mm, the vacuum degree was 3.5 kPa, the number of honeycomb seeding disc holes was 24, the hole diameter was 13 mm, and the seed height was 60 mm, the working performance of the combined seed meter reached the optimal level, which provides a reference for the development of a small seed vegetable precision seeder.

The emergence of nanotechnology and surface engineering techniques provides new opportunities for designing self-lubricant coatings with enhanced properties. In recent years, green coating technologies have played a vital role in environmental preservation. This article mainly reviews five typical types of self-lubricant coatings including MoN coatings, VN coatings, WN coatings and TMN (Transition Metal Nitride) soft-metal coatings, and DLC (Diamond-like Carbon) with lubricant agents deposited by PVD (Physical Vapor Deposition) for the demanding tribological applications, which is the latest research into the green lubricant coatings. Furthermore, it is of great significance for designing the green self-lubricant coatings to adapt the demanding tribological applications to meet the industrial requirements.