Computer vision based non-destructive growth monitoring is essential for lettuce planting management. The texture and coloretc. in lettuce image are?closely related with its growth traits. Previous studies on image-based traits estimation include machine learning-based methods with hand-crafted features and convolutional neural network ( CNN) -based methods. We propose a two-stage methodconsisting of CNN and machine learning for lettuce growth traits estimation, including leaf fresh weight ( LFW ) , leaf dry weight( LDW) ,height ( H) ,diameter ( D) ,and leaf area ( LA) . In the first stage,CNN is trained to automatically extract feature from images.In the second stage,Stacking regression ( random forest,deep forest) is trained to estimate traits based on extracted features by CNN. Experiment results illustrate that the proposed two stage method outperforms CNN - based method with normalized mean square error( NMSE) of 2. 25% ,2. 61% ,1. 63% ,0. 84% ,3. 18% for five traits respectively,and with R2 values of 0. 955 2,0. 957 8,0. 892 1,0. 884 4,0. 936 2,respectively. For height estimation,the introduction of depth image could further enhance performance with NMSE of1. 27% and R2 of 0. 916 1. The result indicates that CNN based feature extraction is valuable for lettuce growth traits estimation.