Soybean is an important source of protein and fat. The increase of soybean yield is playing a significant role in guaranteeing food security and satisfying market demanding. Therefore, rapid screening of soybean varieties with high yield and quality is of great significance to increase the total output of soybean. Leaf area index (LAI), which refers to the gross one-sided leaf area per surface area, is one of the critical phenotypic parameters to characterize crop canopy structure, and it has an important significance to evaluate crop photosynthesis, growth and predict yield. A rapid, non-destructive and efficient estimation of soybean LAI can assist the screening of high-yield varieties. Currently, lots of soybean breeding material plots is one the difficulties in soybean breeding, but traditional manual investigation method is time-consuming, inefficient job with certain degree of subjectivity. Unmanned aerial vehicle (UAV) remote sensing technology has become a research focus on precision agriculture application. It features the advantages of easy construction, low operation and maintenance cost and flexible mobility, and has been used to realize rapid, non-destructive, spatial continuous crop growth monitoring and crop yield estimation. Researches based on low-cost UAV high spatial resolution digital images to estimate crop phenotypic parameters mainly focused on the crop cultivation and management sector. However, there are few researches on crop breeding. The high spatial resolution digital images of the Shengfeng academician workstation of soybean breeding experiment located in Jiaxiang County, Jining City, Shandong Province, China from July to September in 2016 were acquired using a low-cost UAV digital camera system. The obtained UAV data contained the high spatial resolution images of growth periods of R1-R2, R3 and R5-R6. At the same time, the average LAI values of 900 breeding plots on the ground were obtained. Firstly, the digital orthophoto map (DOM) was generated. The generated DOM was calibrated using the image values of black and white calibration tarps in the DOM image and a total of eighteen calibrated variables of R, G, B, MGRVI, RGBVI, GLA, ExG, WI, ExGR, CIVE, VARI, G/R, G/B, R/B, R/(R+G+B), G/(R+G+B) and B/(R+G+B) were calculated based on existing research. Secondly, 70% of the total data pairs of the eighteen variables and corresponding groundmeasured data were used to build models, including the unary linear regression, stepwise regression, total subset regression, partial least squares regression, support vector machine regression and random forest regression, while the remaining data pairs were used for model validation. Finally, the optimal prediction model for LAI was selected by comprehensively considering the determination coefficient (R2), root mean square error (RMSE) and normalized root mean square error (nRMSE) of model building and validating. The results showed that the total subset regression, which included four variables of B, RGBVI, GLA and B/(R+G+B), was the optimal estimation model of LAI. The R2, RMSE and nRMSE of model building and validation were 0.69, 0.99, 17.90% and 0.68, 1.00, 18.10%, respectively. The spatial distribution map of LAI of soybean breeding materials area was generated. Compared with ground-measured values and DOM derived from digital camera images, the distribution map could well reflect the growth status of soybean breeding materials. The results showed that high spatial resolution digital images of soybean breeding materials could be obtained quickly using UAV remote sensing technology. After that, the qualitative and quantitative analysis can be carried out to monitor the status of soybean breeding materials in the study area. In general, the UAV remote sensing technology with digital camera was feasible in predicting the LAI of soybean breeding materials, and it can serve as a rapid, effective and non-destructive way for LAI estimation in large-scale soybean breeding area.