Above-ground biomass and plant nitrogen content play a crucial role in crop growth, development, and yield formation. Therefore, dynamic monitoring of crop growth and nutritional status is of considerable importance. The study used unmanned aerial vehicles to obtain hyperspectral data and above-ground biomass during the budding stage, tuber formation stage, tuber growth and starch accumulation stage, to analyze the correlation and the importance of variable projection between vegetation indices and biomass and plant nitrogen content, and to screen out vegetation indices that are sensitive to biomass and plant nitrogen content combining deep neural network (DNN), partial least squares (PLSR), elastic network regression (ENR), ridge regression (RR) and support vector machine (SVR) to estimate biomass and plant nitrogen content and comparing the effectiveness of different models in estimating biomass and plant nitrogen content. The results showed that (1) the correlation between vegetation indices and both biomass and plant nitrogen content reached 0.01 significant level, and the importance of the variable projection was used to screen out the vegetation indices that were sensitive to biomass and plant nitrogen content; (2) Comparing the remote sensing estimation models for the five growth stages, the best model for biomass and plant nitrogen content was constructed at the tuber formation stage, the worst model for biomass was estimated at the present bud stage, and the worst model for plant nitrogen content was estimated at the tuber growth stage. (3) The optimum biomass model constructed in the tuber formation stage using the PLSR method was modelled with R², RMSE and NRMSE was 0.60, 235.65 kg·hm^-2 and 0.15 kg·hm^-2 respectively, and validated with R², RMSE and NRMSE was 0.58，344.72 kg·hm^-2 and 0.26 kg·hm^-2，The optimum plant nitrogen content model constructed during tuber formation stage using RR method was modelled with R²，RMSE and NRMSE was 0.74, 0.31% and 0.15%，validated R², RMSE and NRMSE was 0.77, 0.58% and 0.28%. Comprehensively comparing the DNN, PLSR, ENR, RR, and SVR algorithms for estimating biomass and plant nitrogen content models, the accuracy of the estimated plant nitrogen content model is found to be better than that of the estimated biomass model. The plant's nitrogen content can be used to more effectively monitor crop growth and nutritional characteristics, providing a reference for informed agricultural management.