Apple grading is an essential part of the apple marketing process to achieve high profits. In this paper, an improved YOLOv5 apple grading method is proposed to address the problems of low grading accuracy and slow grading speed in the apple grading process and is experimentally verified by the designed automatic apple grading machine. Firstly, the Mish activation function is used instead of the original YOLOv5 activation function, which allows the apple feature information to flow in the deep network and improves the generalization ability of the model. Secondly, the distance intersection overUnion loss function (DIoU_Loss) is used to speed up the border regression rate and improve the model convergence speed. In order to refine the model to focus on apple feature information, a channel attention module (Squeeze Excitation) was added to the YOLOv5 backbone network to enhance information propagation between features and improve the model’s ability to extract fruit features. The experimental results show that the improved YOLOv5 algorithm achieves an average accuracy of 90.6% for apple grading under the test set, which is 14.8%, 11.1%, and 3.7% better than the SSD, YOLOv4, and YOLOv5s models, respectively, with a real-time grading frame rate of 59.63 FPS. Finally, the improved YOLOv5 apple grading algorithm is experimentally validated on the developed apple auto-grader. The improved YOLOv5 apple grading algorithm was experimentally validated on the developed apple auto grader. The experimental results showed that the grading accuracy of the automatic apple grader reached 93%, and the grading speed was four apples/sec, indicating that this method has a high grading speed and accuracy for apples, which is of practical significance for advancing the development of automatic apple grading.

Aiming at the free-form curve contour tracking error problem of CNC machining, a cross-coupled controller based on Fuzzy PID using an improved PSO algorithm is proposed. First, the exact formulas of the linear and circular arc contour errors under a plane trajectory are derived, and an estimation method for the free curve profile error under a plane trajectory based on the principle of approximate approximation of preferred points is designed. Second, a cross-coupled controller based on Fuzzy PID using an improved PSO algorithm is achieved. An improved PSO algorithm, whose learning factor is adjusted with the inertia weight, is used to optimize the controller parameters and achieve contour tracking of the free curve. Finally, a two-dimensional biaxial experimental platform is used to test the proposed method. The results demonstrate that the optimal gain parameters obtained by the proposed strategy can significantly improve contour control accuracy in biaxial contour tracking tasks. The proposed contour error control scheme can achieve not only a nearly perfect contouring error estimation, but also an obvious promotion of contouring accuracy.