Estimation of potato biomass based on UAV digital images

Accurate estimation of crop biomass by plant height and Vegetation Coverage (VC) is of great significance in agricultural production and has a strong guiding significance for agricultural managers. It is necessary to use an effective method to estimate the biomass of field crops quickly and accurately. Taking the potato in Xiaotangshan National Precision Agricultural Research Demonstration Base as the research object and conducted a field experiment between March and July 2019. Digital images were taken by Unmanned Aerial Vehicle (UAV) and ground camera from the field at budding, tuber formation, tuber growth, starch accumulation, and maturity period and measured plant height, above-ground biomass, and three-dimensional coordinates of Ground Control Points (GCPs) were obtained by ground survey. Firstly, the Digital Surface Model (DSM) is generated based on GCPs to calculate the plant height and compared the results with field measurements. Secondly, Measured and estimated values of VC were calculated through ground and UAV digital images and compared between the results. Then, the correlation analyses between biomass and extraction values of plant height, VC, their product, and eleven Vegetation Indices (VIs). Six VIs and three agronomy parameters were selected for each growth stage, respectively. Finally, the selected VIs and three agronomy parameters were used as modeling factors, and the biomass was estimated by Linear Regression (LR), Partial Least Square Regression (PLSR), Random Forest (RF) algorithm, and Support Vector Machine (SVM), and the models constructed by the remote sensing data were compared to optimize the model. The results showed that the extraction values of plant heigh from DSM agreed well with the measurements, the coefficient of determination was 0.86 and the normalized root mean square error was 13.42% throughout the growth period. Measured and predicted values of VC stayed highly relevant, the coefficient of determination was 0.84 and the normalized root mean square error was 15.76% throughout the growth period. Through three agronomy parameters, analyzing the effect of the modeling and verification set, the accuracy of the Linear Regression (LR) model with extraction values of plant heigh multiply predicted values of VC as modeling factors was significantly better than that of extraction values of plant heigh or predicted values of VC, however, the accuracy of estimating biomass model with extraction values of plant height was the worst. In different growth periods, the performance of estimating biomass by LR had gradually increased from the budding stage to the tuber growth stage and reduced from the starch accumulation stage to the maturity stage. To compare capabilities of PLSR, RF, and SVM to estimate potato biomass, this study compared the accuracy of models for different growth periods using four variables, for example, VIs combined with extraction values of plant height, VIs combined with extraction values of VC and three variables as one. For PLSR, RF, and SVM models, the accuracy of modeling and verification showed a trend of first increasing and then decreasing when using the same kind of variables as model factors. Comparison of the accuracy of models was contrasted by different methods with the same variable at five periods, it is found that VIs incorporating the plant height and VC into estimation model significantly improved the biomass estimation. Comparison with the measured biomass showed that the coefficient of determination, the normalized root mean square error of PLSR model was 0.628 5 at bud period, 0.658 4 at tuber formation period, 0.681 4 at tuber growth period, 0.653 2 at starch accumulation period, 0.548 8 at maturity period, respectively. The PLSR model is superior to the RF and SVM model which the coefficient of determination was 0.538 5, 0.603 3, 0.632 2, 0.615 9, 0.542 4 and 0.445 1, 0.521 1, 0.601 3, 0.574 3, 0.538 4, respectively. In summary, the biomass of potato was quickly estimated using UAV digital images data by the PLSR method combined with VIs, plant height, and VC as a whole in different growth periods and provided technical support for effectively monitoring crop growth and accurately predicting yield.