To improve the accuracy and reliability of orchard spraying robots, an integrated navigation system was developed, consisting of a real-time kinematic positioning-Beidou satellite navigation system (RTK-BDS) receiver, an inertial measurement unit (IMU), a navigation controller, and servo motors. Using the loose coupling combination method, an error Kalman filter algorithm based on the measurement of position and heading angle is implemented to correct the error of the inertial measurement unit in real time. Combining the kinematics model and the pure pursuit model of the spraying robot, a path-tracking control algorithm is proposed. Path planning was conducted according to the terrain characteristics of orchards. Field experiments were carried out on a spraying robot to evaluate the proposed auto-navigation system. The results showed that when the spraying robot was static, the positioning performances of BDS alone and that of the BDS/IMU combined system were similar, the positioning error was less than 1.5 cm, and the heading angle errors were within 0.3°; when the spraying robot moving alone to a straight line at the speed of 0.4 m/s, the position error of the navigation system only using BDS was less than 5.29 cm, the heading angle error was within 3°, while the position error of BDS/IMU integrated navigation system was less than 2.49 cm, and the heading angle error was within 2°. The accuracy of BDS/IMU integrated navigation system is significantly improved. When the orchard spraying robot was moving at the speed of 0.4 m/s, the maximum offset error was lower than 10.77 cm, the average offset error was not higher than 3.55 cm, and the root mean square error (RMSE) of the lateral deviation was 1.19 cm. The results showed that the proposed auto-navigation system could make the spraying robot track the pre-set path smoothly and stably.

In order to detect kiwifruit quickly and accurately in orchard environments for the picking robot, this paper proposed a detection method based on a lightweight YOLOv4-GhostNet network. The implementations of the method are as follows: The original CSP-Darknet53 backbone network model was replaced by GhostNet, a feature layer facilitating small object detection was introduced in the feature fusion layer, and part of the ordinary convolution was replaced by a combination of 1 × 1 convolution and depth-separable convolution to reduce the computational pressure caused by the fused feature layer. The parameters of the new network are reduced, and the generalization ability of the model is improved by loading pre-training weights and freezing some layers. The trained model was tested, and the results showed that the detection performances were better than that of the original YOLOv4 network. The F1 value, map, and precision were improved on the test set, which were 92%, 93.07%, and 90.62%, respectively. The size of weight parameters was reduced to 1/6 of the original YOLOv4 network, and the detection speed reached 53 FPS. Therefore, the method proposed in this study shows the features of fast recognition, lightweight parameters, and high recognition accuracy, which can provide technical support for vision systems of kiwifruit picking robots.

Sweetness or sugar content, represented by soluble solids contents (SSC), is a vital quality trait in watermelon breeding which can be assessed by the refractive index method. However, sampling watermelon juice out of the pulp is a process that is both labor-intensive and error-prone. In this study, we developed an automatic SSC measurement system for watermelon breeding to improve efficiency and decrease costs. First, we built an automatic cutting system to cut watermelons into precise halves, in which a laser rangefinder is used to measure the distance from the upper surface of the watermelon to itself, and thus, the diameter is estimated. The experiments showed a high correlation between the estimated diameters and the ground truths, with and . Then, we built an automatic Brix measurement system to obtain the Brix data from a central point on the watermelon’s section, where an image analysis procedure is applied to locate the testing point. This is then transformed to the camera coordination system, and a refractometer is driven by a 3-axis robotic arm to reach the testing point. Brix measurement experiments were conducted using three vertical gaps and four lateral gaps between the probe of the refractometer and the pulp. The result showed that the best parameters were a vertical gap of 4 mm and a lateral gap of 2 mm. The average accuracy reached 98.74%, which indicates that this study has the potential to support watermelon breeding research.