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In response to the current focus on single growth stage phenotype characteristics in most phenotype research, which makes it difficult to accurately monitor plant growth throughout whole growth cycle, a high-accuracy identification method for key phenotypic parameters of hanging watermelon throughout whole growth cycle was proposed by combining multiple deep learning methods and machine vision technologies. In the seedling stage, leaf area calculation model based on Leaf SAM and leaf count model based on Xception were established, and the experiments results showed that the R2 of the leaf area and leaf count models were 0.96 and 0.98, and the root mean square error（RMSE）was 2.98 cm2 and 0.14, respectively. During the elongation period, plant height measurement model based on YOLO v5 and binocular vision principles, as well as stem thickness calculation model based on OpenCV, were established separately, and the experiment results showed that the R2 of the plant height and stem thickness measurement model were 0.94 and 0.92, and the RMSE was 4.18 cm and 0.17 mm, respectively. In the fruiting and ripening stages, a fruit projection area calculation model based on UNet was established, and the experiment results showed that the R2 of the fruit projection model and the RMSE were 0.99 and 9.85 cm2, respectively. The above results showed that the linear relationship between the calculated and manually measured values was significant, and the comprehensive error was low, which can effectively calculate the key phenotypic parameters throughout the whole growth cycle of hanging watermelon.