A residual network based on dual-channel attention mechanism is designed to address the difficulty in extracting single and segmented seedling dataset images from the channel attention mechanism feature of the SENet network, which integrates the channel attention mechanism and spatial attention. The mechanism module can obtain the channel and spatial dimension feature weights simultaneously to enhance the feature learning ability of the network. To address the problem of missing the target in the segmented sample data, a random erasure method is proposed. Experiments on the self-made plug seedling Plant_seed dataset demonstrate that the improved network ResNet34+CBAM_basic_conv, which introduces the attention mechanism module between the ResNet34 network residual module and the conv*_x module, reaches the optimal accuracy of 93.8%. The error rate of the model classification drops after some images in the dataset are randomly erased, demonstrating the excellent performance of the proposed method.