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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., (© 2023. The Author(s).)

In order to study the mechanism of bract peeling and crushing of a corn harvester, a bract stripping and crushing device was designed that mainly consists of a stripping device, a crushing device, and a frame. The kinematics and dynamics of the roller of bract stripping device were analyzed, and the conditions of bract stripping were obtained. Single factor tests of stripping roller speed and crushing roller speed were carried out. On the basis of single factor tests, orthogonal experiments were carried out to determine crushing roller speed, distance between crushing device axis and stripping device axis, stripping roller speed, and offset angle of stripping roller. The orthogonal experiments showed that the optimum parameters of the device were obtained: the speed of the crushing drum was 1,100 r/min, the axis distance between the stripping and crushing devices was 180 mm, the speed of the stripping roller was 400 r/min, and the offset angle of the stripping roller was 5 degrees. On the basis of orthogonal experiment, three, five, seven, nine and eleven corn feeding and high-speed photography experiments were carried out respectively. The results showed that when three ears of corn were fed at the same time, the effect of corn bract stripping and crushing was the best with the increase of corn number. The stripping roller only grasps most of the bracts of corn at 0.019 s, after stripping the bracts into the crushing device, the bracts were crushed after 0.077 s. The crushing time of bracts was approximately four times as long as that of the bracts., Competing Interests: I have declared that no competing interests exist.