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As an indicative feature of crop growth and development, the dynamic change of fresh weight of crops in facility cultivation is one of the important indicators for non-destructive monitoring of vegetable growth. Hydroponic vegetables can be directly weighed out of water to achieve non-destructive monitoring of growth, but it is difficult to achieve fresh weight by non-destructive measurement in soil or matrix. To solve this problem, the fresh weight estimation method based on the combination of phenotypic parameters and environmental parameters was proposed to estimate the fresh weight of individuals and groups of lettuce in solar greenhouse. Firstly, the environmental parameters of the whole life cycle of lettuce were monitored. The multi sample images and the fresh weight of some samples were collected during the growth of the first batch of lettuce. The shape, color, texture and other characteristics of lettuce in different growth periods were extracted from the sample images, and the accumulated heat product in the environmental information was calculated. Then, the relationship model between the parameters of phenotype and environment and fresh weight of lettuce was established by Gaussian process regression. Finally, the sample data of the second batch of groups of lettuce were collected to predict the fresh weight of individuals and groups of lettuce at three growth stages based on the above model, so as to verify the generalization ability and reliability of the fresh weight estimation model. The results showed that compared with support vector machine, linear regression, ridge regression and neural network, the determination coefficient of Gaussian process model was 0.9493, and the mean of relative error was 11.50%, while the standard deviation of relative error was 11.21%. In the model generalization ability test, the average value of relative error of prediction of fresh weight of groups of lettuce was smaller than that of individuals of lettuce,and the difference of them were 4.44, 5.71 and 5.89 percentage points at the three growth states. The average value and standard deviation of predicted fresh weight of groups of lettuce was gradually decreased with the increase of groups. The fresh weight data of groups predicted by this method can provide data support for the cultivation and management decision of substrate cultivated green leafy vegetables.