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Phenotypic parameters of plants are quantitatively indicated, describing the morphology, structure, and physiological characteristics of plants, unveiling the growth patterns and relationships with environmental factors. Issues such as significant data errors, plant damage, high costs, and extensive data volume were exhibited by existing manual measurement and laser scanning-based methods for extracting plant phenotypic parameters. Therefore, an automatic extraction method for phenotypic parameters of Anthurium andraeanum Linden plants based on YOLO v8 and CycleGAN was proposed. The method included the follows: YOLO v8 was enhanced with the convolutional block attention module to improve the model’s feature extraction capabilities for detecting and segmenting Anthurium andraeanum Linden leaves;the Grabcut algorithm was utilized to eliminate background features from segmented images, and the VGG model was employed for classification to distinguish intact and missing Anthurium andraeanum Linden leaves;the convolutional block attention module and feature pyramid network were introduced into the CycleGAN generator to enhance multi-scale feature extraction capabilities, incorporating the SmoothL1 loss function to enhance model stability and repair missing Anthurium andraeanum Linden leaves;a phenotypic parameters extraction algorithm (PPEA) was proposed to automatically extract leaf length, leaf width, and leaf area of Anthurium andraeanum Linden plants. The proposed methods were compared and analyzed by using a dataset of 650 self-collected images. Experimental results demonstrated the effectiveness of the proposed approach in automatically extracting phenotypic parameters of Anthurium andraeanum Linden plants.