Abstract
The health information acquisition of plug seedlings plays an important role in automatic seedling transplanting. This study proposed the use of a mobile inspection and transplanting machine operation method on the seedbed. Compared with the operation of a fixed transplanter, this method can reduce the workload of tray transportation. When the performing mobile picks and supplements the seedlings, the plug seedling detection system needs to obtain a large-field seedbed image and extract and locate multiple plug trays. Then, the system evaluates the health of the plug seedlings. This paper studied and compared the stitching effect of large-field seedbed images based on block area matching, Harris corner detection, and SURF feature point detection. Based on the large field seedbed image, the concentric rectangular frame template matching algorithm was designed and developed. This algorithm can realize the precise positioning of multiple plug trays on the large field seedbed image. Thus, the health status of plug seedlings was further assessed according to the area of seedlings in each cell. A 200-cell tray seedbed was the research object, and a digital camera was used to capture the partitioned image of the seedbed in multiple sections. The image stitching algorithm was used to obtain the stitched image of the large-field seedbed. After image preprocessing, including grayscale conversion, binary segmentation, and Gaussian filtering, the concentric rectangular frame template matching algorithm was used to stitched images to achieve the scanning positioning of the multiple plug trays. Furthermore, based on the positioning of the plug tray, the plug tray area was divided, and then the health status of plug seedlings was evaluated. Experiments have proved that the accuracy rate of seedling health information detection is 98.7%. Such results provide basis for precision management of seedling replenishment operations on the seedling bed and meets the requirements of actual operations.
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