Abstract: Abstract: Leaf area index (LAI) and above ground biomass (AGB) are two critical traits indicating the growth of winter wheat. Currently, non-destructive methods for measuring LAI and AGB heavily are subjected to limitations that the methods are susceptible to the environmental noises and greatly depend on the manual designed features. In this study, an easy-to-use growth-related traits estimation method for winter wheat at early growth stages was proposed by using digital images captured under field conditions and Convolutional Neural Network (CNN). RGB images of winter wheat canopy in 12 plots were captured at the field station of Shangqiu Academy of Agriculture and Forestry Sciences, Henan, China. The canopy images were captured by a low-cost camera at the early growth stages. Using canopy images at early growth stages as input, a CNN structure suitable for the estimation of growth related traits was explored, which was then trained to learn the relationship between the canopy images and the corresponding growth-related traits. Based on the trained CNN, the estimation of LAI and AGB of winter wheat at early growth stages was achieved. In order to compare the results of the CNN, conventionally adopted methods for estimating LAI and AGB in conjunction with a collection of color and texture feature extraction techniques were used. The conventional methods included a linear regression model using canopy cover as the predictor variable (LR-CC), Random Forest (RF) and Support Vector Machine Regression (SVR). The canopy images of winter wheat were captured at early growth stages, resulting in the existence of pixels representing non-vegetation elements in these images, such as soil. Therefore, it was necessary to perform image segmentation of vegetation for the compared methods prior to feature extraction. The segmentation was achieved by Canopeo. The linear regression was used to compare the accuracy of the methods. Normalized Root-Mean-Squared error (NRMSE) and coefficient of determination (R2) were used as the criterion for model evaluation. Results showed the CNN demonstrated superior results to the compared methods in the two metrics. Strong correlations could be observed between the actual measurements of traits to those estimated by the CNN. The estimation results of LAI had R2 equaled to 0.825 6 and NRMSE equaled to 23.33%, and the results of AGB had R2 equaled to 0.791 7 and NRMSE equals to 24.37%. Compare to the comparative methods, the CNN was a more direct method for AGB and LAI estimation. The image segmentation of vegetation was not necessary because the CNN was able to use the important features to estimate AGB and LAI and ignore the non-important features, which not only reduced the computation cost but also increased the efficiency of the estimation. In contrast, the performances of the compared estimating methods greatly depended on the results of image segmentation. Accurate segmentation results guaranteed accurate data sources to feature extraction. However, canopy images captured under real field conditions were suffering from uneven illumination and complicated background, which was a big challenge to achieve robust image segmentation of vegetation. It was revealed that robust estimation of AGB and LAI of winter wheat at early growth stages could be achieved by CNN, which can provide support to growth monitoring and field management of winter wheat.