A Method for Counting Leaves of Cabbage Seedlings Based on Instance Segmentation

Judging the seedling age of cabbage seedlings at the seedling stage is helpful for the production management of cabbage seedlings, and is of great significance for guiding the fertilization amount of seedling production operations. In order to realize the accurate judgment of the number of cabbage leaves in the complex environment of the nursery greenhouse, in view of the problem that the target size of the leaves of the cabbage seedlings is small and difficult to identify, a method for counting the leaves of the cabbage seedlings based on instance segmentation was proposed. The model structure is based on the Mask R-CNN instance segmentation model, using Resnet50 as the feature extractor, adding deformable convolution to improve the feature extraction capability of the model, and selecting the category cross entropy as the loss function. The model is verified on the cabbage seedling data set constructed by self-collection. The proposed method is better than yoloV3 and FPN. The coefficient of determination, root mean square error and mean absolute error of the model trained in this paper reach 0.93, 6.24, and 4.63, compared with yoloV3 and FPN. The original network, the counting accuracy is improved. The method can accurately identify the number of leaves in each growth stage of cabbage seedlings, and provide an effective theoretical basis for the informatization of facility cabbage seedling production.