Maize Seedling and Core Detection Method Based on Mask R-CNN

【Objective】 In view of the problem of low recognition accuracy of corn seedlings and plant cores in the actual field environment, a corn seedling canopy segmentation algorithm based on improved Mask R-CNN is proposed in the hope of satisfying the identification requirements for target fertilization in precision operations, thus improving the use efficiency of chemical fertilizers, and reducing environmental pollution. 【Methods】 Firstly, the field corn seedling pictures were collected and the data were enhanced to generate the field data set. Secondly, the segmentation algorithm was trained by using ResNeXt50/101-FPN as feature extraction network, and compared with the training accuracy results of the original ResNet50/101-FPN. Finally, the canopy recognition algorithm was compared and verified by pictures with different light intensities and different degrees of occlusion. 【Results】 Under different light intensities, the average recognition accuracy of the target without accompanying weeds was higher than 95.5%, and the segmentation accuracy was 98.1%; when there was an overlap between the associated weeds and corn seedlings, the average recognition accuracy of the target was higher than 94.7%, and the segmentation accuracy reached 97.9%. After image testing, the average time to detect one frame of image was 0.11 s. 【Conclusion】 The results show that the maize seedling and plant core detection algorithm of Mask R-CNN in this paper has higher accuracy and segmentation accuracy, which is more suitable for target detection in different light intensities and crossover overlap of seedlings and grass with associated weeds.