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

文献类型： 会议论文

第一作者： Ning Zhang

作者： Ning Zhang 1 ; Huarui Wu 1 ; Huaji Zhu 1 ; Yisheng Miao 1 ; Xiang Sun 1 ;

作者机构： 1.Big Data Intelligence Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China|Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China|Key Laboratory of Agricultural Information Digital Rural Technology, Ministry of Agriculture and Rural Affairs, Beijing, China

关键词： Deformable models;Production management;Convolution;Green products;Feature extraction;Entropy;Data models;Root mean square;Artificial intelligence;Residual neural networks

会议名称： Asian Conference on Artificial Intelligence Technology

主办单位：

页码： 1-5

摘要： 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.

分类号： tp18-53

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