Fan Yiguang, Feng Haikuan, Jin Xiuliang, Yue Jibo, Liu Yang, Li Zhenhai, Feng Zhihang, Song Xiaoyu, Yang Guijun
Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China.
School of Geographic, Liaoning Technical University, Fuxin, China.
Front Plant Sci. 2022 Oct 18;13:1012070. doi: 10.3389/fpls.2022.1012070. eCollection 2022.
Plant nitrogen content (PNC) is an important indicator to characterize the nitrogen nutrition status of crops, and quickly and efficiently obtaining the PNC information aids in fertilization management and decision-making in modern precision agriculture. This study aimed to explore the potential to improve the accuracy of estimating PNC during critical growth periods of potato by combining the visible light vegetation indices (VIs) and morphological parameters (MPs) obtained from an inexpensive UAV digital camera. First, the visible light VIs and three types of MPs, including the plant height (), canopy coverage (CC) and canopy volume (CV), were extracted from digital images of the potato tuber formation stage (S1), tuber growth stage (S2), and starch accumulation stage (S3). Then, the correlations of VIs and MPs with the PNC were analyzed for each growth stage, and the performance of VIs and MPs in estimating PNC was explored. Finally, three methods, multiple linear regression (MLR), k-nearest neighbors, and random forest, were used to explore the effect of MPs on the estimation of potato PNC using VIs. The results showed that (i) the values of potato and CC extracted based on UAV digital images were accurate, and the accuracy of the pre-growth stages was higher than that of the late growth stage. (ii) The estimation of potato PNC by visible light VIs was feasible, but the accuracy required further improvement. (iii) As the growing season progressed, the correlation between MPs and PNC gradually decreased, and it became more difficult to estimate the PNC. (iv) Compared with individual MP, multi-MPs can more accurately reflect the morphological structure of the crop and can further improve the accuracy of estimating PNC. (v) Visible light VIs combined with MPs improved the accuracy of estimating PNC, with the highest accuracy of the models constructed using the MLR method (S1: = 0.79, RMSE=0.27, NRMSE=8.19%; S2: = 0.80, RMSE=0.27, NRMSE=8.11%; S3: = 0.76, RMSE=0.26, NRMSE=8.63%). The results showed that the combination of visible light VIs and morphological information obtained by a UAV digital camera could provide a feasible method for monitoring crop growth and plant nitrogen status.
植物氮含量（PNC）是表征作物氮营养状况的重要指标，快速高效地获取PNC信息有助于现代精准农业中的施肥管理和决策。本研究旨在探索通过结合从廉价无人机数码相机获得的可见光植被指数（VIs）和形态参数（MPs）来提高马铃薯关键生长时期PNC估算精度的潜力。首先，从马铃薯块茎形成期（S1）、块茎生长期（S2）和淀粉积累期（S3）的数字图像中提取可见光VIs和三种类型的MPs，包括株高（）、冠层覆盖度（CC）和冠层体积（CV）。然后，分析每个生长阶段VIs和MPs与PNC的相关性，并探讨VIs和MPs在估算PNC方面的性能。最后，使用多元线性回归（MLR）、k近邻和随机森林三种方法，探索MPs对利用VIs估算马铃薯PNC的影响。结果表明：（i）基于无人机数字图像提取的马铃薯和CC值准确，生长前期的精度高于生长后期。（ii）利用可见光VIs估算马铃薯PNC是可行的，但精度有待进一步提高。（iii）随着生长季的推进，MPs与PNC的相关性逐渐降低，PNC估算变得更加困难。（iv）与单个MP相比，多个MPs能更准确地反映作物的形态结构，可进一步提高PNC估算精度。（v）可见光VIs与MPs相结合提高了PNC估算精度，使用MLR方法构建的模型精度最高（S1：=0.79，RMSE=0.27，NRMSE=8.19%；S2：=0.80，RMSE=0.27，NRMSE=8.11%；S3：=0.76，RMSE=0.26，NRMSE=8.63%）。结果表明，可见光VIs与无人机数码相机获得的形态信息相结合，可为监测作物生长和植株氮素状况提供一种可行的方法。
