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• An anti-lodging machine, comprising an anti-lodging device and an AGV (Automated Guided Vehicle) was designed to replace manual methods. • A machine vision system was developed to aim the anti-lodging device at the crown buds. • The YOLOv5s algorithm is enhanced for detecting immature pineapple crown buds. • The mAP_0.5 of the improved YOLOv5s algorithm is 3.71 %, 1.69 %, and 1.19 % higher than the original YOLOv5s, SSD, and Faster R-CNN, respectively. • The operating speed of this intelligent anti-lodging method is 2.7 times faster than manual binding. Sunscorch, a common disease in pineapple cultivation, is caused by direct sunlight on the surface of lodging pineapple fruits. To mitigate this issue, pineapple fruits are often bagged or supported with fixing rods. This paper presents the design of an anti-lodging machine, comprising an anti-lodging device and an AGV (Automated Guided Vehicle), and develops a machine vision system to precisely aim the anti-lodging device at the crown buds. The YOLOv5s algorithm is enhanced for detecting crown buds. The improvement of the YOLOv5s algorithm includes the introduction of the attention mechanism CBAM to enhance feature extraction, the addition of BiFPN and GhostNet to strengthen feature fusion and to reduce detection time and computational cost. By transforming pixel coordinates, the relative coordinates between the crown bud and the device are calculated to achieve accurate targeting. The experiments demonstrate that the precision, recall, and map_0.5 of the improved YOLOv5s algorithm are 91.89 %, 92.25 %, 94.01 %, and the detection speed is 15.71 ms/image. The average detection speed is 1.14 ms/image and 9.45 ms/image faster than that of SSD and Faster R-CNN, benefiting from a parameter number only 3/4 and 3/7 of SSD and Faster R-CNN, respectively. The results of contrast experiments indicate that, although the lodging prevention rate of this intelligent anti-lodging method is 4 % lower than the manual binding, its operating speed is 2.7 times faster. [ABSTRACT FROM AUTHOR]

• The improvements made to the YOLOv5 algorithm include the addition of a small object detection layer in the neck layer. • The modifications were made to the original loss function to enhance training stability and increase training speed. • The precision rate of the enhanced YOLOv5 surpasses that of SSD, Faster RCNN, and the original YOLOv5 by 9.42%, 20.41% and 3.62%, respectively. • The averages for the row recognition rate, navigation line extraction accuracy rate and angle error were recorded at 89.29%, 85.74% and 3.54°, respectively. Moreover, the row recognition rate and navigation line extraction accuracy rate of the proposed algorithm surpassed those of the least squares algorithm and random sample consensus algorithm by 7.69% and 6.8%, as well as 17.56% and 14.94%, respectively. The unmanned pineapple harvester is required to operate efficiently along crop rows. This study presents a novel approach for extracting visual navigation line in pineapple fields using an enhanced YOLOv5 algorithm. The objective is to overcome challenges related to positioning accuracy rate and stability encountered by unmanned pineapple harvesters. The improvements made to the YOLOv5 algorithm include the addition of a small object detection layer in the neck layer. Furthermore, modifications were made to the original loss function to enhance training stability and increase training speed. Experimental results demonstrate that the enhanced YOLOv5 model achieved a 3.62 % increase in training precision rate and a 2.15 % increase in recall rate compared to the original model. With an image resolution of 960 × 1080 pixels, the average detection speed reached 17.35 frames per second (fps). The enhanced YOLOv5 algorithm was utilized to recognize and extract feature points representing pineapple rows. A clustering algorithm was employed to classify these feature points by row, while a modified shortest distance algorithm was applied to fit the pineapple crop row centerline and calculate the optimal navigation line. Field experiments revealed an average extraction accuracy rate of 89.13 % and 85.32 % for sunny and cloudy weather in high-density crops, respectively, with an average accuracy rate of 85.74 %, row recognition accuracy rate of 89.29 % and angle error of 3.54° in different density crops, respectively. This algorithm offers a method for obtaining navigation paths for unmanned pineapple harvesters. [ABSTRACT FROM AUTHOR]