Low-damage fetching method for pepper seedlings based on Res-Unet

Automatically, seedling fetching is a critical link in the automatic planting of facility agriculture. However, the damage caused by the mechanical claws to the seedling during the seedling fetching process seriously affects the survival rate of the seedling after being planted. To reduce the damage of the mechanical claw on the seedling leaves during seedling fetching, this paper proposes a low-loss seedling fetching method and designs a set of end-effectors suitable for the low-loss seedling fetching method. The low-damage seedling fetching way consists of a semantic segmentation algorithm and a seedling fetching point estimation algorithm, which can position the seedling leaves and calculate the optimal fetch seedling angle of the mechanical claw so that the mechanical claw can avoid seedling leaves during the seedling fetching to reduce the damage to the seedling. The backbone network of the Res-Unet model is composed of Resnet50, which can accurately extract the characteristics of plug seedlings. Comparative test results with other models show that our semantic segmentation model has high accuracy in positioning seedlings, with an MIoU value of 96.49%. Finally, a seedling fetching verification experiment was conducted, and the seedling damage rate was 4.16%. Compared with the ordinary seedling fetching method, the damage rate of the seedling was reduced by 15.28%. This method can minimize damage during the seedling fetching process and provide technical support for automated planting.