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Leaf Area Index (LAI) and biomass (BIO) are essential agronomic parameters that reflect the growth of potatoes and are related to their biomass. Their precise estimation is capable of monitoring crop growth, guiding field management, and optimizing planting spatial patterns. Traditional potato leaf area indexing and biomass estimation primarily rely on field sampling surveys. This method is low in efficiency, high in cost, and limited by the number of samples. It cannot accurately reflect potato growth and meet the real-time estimation needs of large areas. Compared to the use of satellite remote sensing data (RSD) for estimating LAI and biomass, research on estimating these two phenotypic parameters using crewless aerial vehicle (UAV) RSD is relatively immature. Research on estimating crop growth index parameters by remote sensing primarily focuses on data obtained from specific types of sensors, targeting specific growth stages to compare and analyze the accuracy of different methods. However, there are few estimates of the impact of optimizing the best data types and optimal growth stage for LAI and biomass estimation by comparing and analyzing different sensor data and different growth stages. Multi-sensor integration technology has made it possible to study different crop phenotype information and estimate the best data type and optimal growth stage in crop phenotypic data estimation, establishing it as a new hot spot in the field. This paper integrates high-definition digital cameras and imaging hyperspectrometers on the UAV platform to obtain digital images and hyperspectral data simultaneously, along with ground-measured potato leaf area index and biomass data. Using the partial least squares regression (PLSR), random forest (RF), support vector machine (SVM), and backpropagation (BP) neural network methods, we got digital images and hyperspectral data from different stages of growth, put together a digital image index and a vegetation index, and looked at how they related to LAI and BIO. Then, we chose the index that had the strongest correlation. To establish LAI and biomass estimation models at various growth phases, this paper compared and analyzed the estimation impacts of various data types and models at various growth phases. It then selected the best data types for LAI estimation and biomass estimation at different growth stages, as well as the best growth phases for LAI and biomass estimation. The outcomes indicated that when potato LAI was estimated, the mean values of R2 and RMSE of the four estimation models were 0.75 and 0.30 Kg/mu at the tuber growth stage, respectively, and the estimation effect was the best, indicating that this was the best growth phase for LAI estimation. The average values of R2 and RMSE in the LAI estimation model using the hyperspectral vegetation index were 0.73 and 0.33 Kg/mu, respectively, indicating that hyperspectral data was the best data type for LAI estimation. When potato biomass was estimated, the mean values of R2 and RMSE of the four methods were 0.67 and 15.25 Kg/mu, respectively, at the tuber growth stage, which were better than other growth phases, demonstrating that this was the best growth phase for biomass estimation. The average values of R2 and RMSE of the biomass estimation model using the hyperspectral vegetation index were 0.67 and 20.08 Kg/mu, respectively, indicating that the hyperspectral data was the best data type for biomass estimation. The average values of R2 of the LAI and biomass estimation model at the maturity stage were only 0.56 and 0.36, both of which indicated poor estimation effects. Our study can serve as a guide to selecting the most effective method for estimating parameters for essential indexes in crop growth monitoring.

【Objective】 Precision agriculture requires a quick and accurate evaluation of the response of yield to managements at different growing stages, which is challenging at the large scales because of the heterogeneity of plants and soils. Remote sensing technologies can plug this gap, and the purpose of this paper is to investigate the feasibility of using unmanned aerial vehicle (UAV) to evaluate the variation in winter wheat yield in response to change in nitrogen fertilization. 【Method】 Canopy spectral information of winter wheat under different nitrogen fertilizations was measured using a multispectral sensor mounted on a UAV at heading, flowering and filling stages, from which we extracted the vegetation indexes. Stepwise regression and random forest models were used to screen the optimal indexes for estimating the wheat yield. 【Result】 Combination of green (G), blue (B), modified soil-adjusted vegetation index 2 (MSAVI2) and soil-adjusted vegetation index (SAVI) at heading stage works best to predict the yield with low AIC (Akaike Information Criterion) and R2=0.65. Combination of excess green (ExG), near infrared (NIR), normalized difference index (NDI), B and modified soil-adjusted vegetation index 2 (MSAVI2) at flowering stage is the best when using the random forest algorithm to predict the yield with low AIC and R2=0.71. The random forest regression model using all vegetation features at filling stages gave the most accurate prediction of the yield with R2=0.76, compared with R2=0.76 when using G, B, ExG, excess red (ExR), normalized difference vegetation index (NDVI), ratio vegetation index (RVI) and normalized difference vegetation index (TNDVI) only. Prediction using B at heading stage, ExG at flowering stage, NIR at filling stage, NDI at filling stage, and excess green-excess red (EXG-EXR) at filling stage predicted a yield with R2=0.76. 【Conclusion】 Combination of the vegetation indexes measured using multispectral sensors at different growing stages can predict the ultimate winter wheat yield, but the accuracy varies. Best results are achievable using a combination of different indexes measured at different growing stages.