文献类型： 外文期刊

作者： Fan, Yiguang ¹ ; Feng, Haikuan ¹ ; Jin, Xiuliang ⁴ ; Yue, Jibo ⁵ ; Liu, Yang ¹ ; Li, Zhenhai ⁷ ; Feng, Zhihang ⁸ ; Song, Xiaoyu ¹ ; Yang, Guijun ¹ ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing Agr, Minist Agr & Rural Affairs, Beijing, Peoples R China

2.Liaoning Tech Univ, Sch Geog, Fuxing, Peoples R China

3.Nanjing Agr Univ, Coll Agr, Nanjing, Peoples R China

4.Chinese Acad Agr Sci, Inst Crop Sci, Key Lab Crop Physiol & Ecol, Minist Agr, Beijing, Peoples R China

5.Henan Agr Univ, Coll Informat & Management Sci, Zhengzhou, Peoples R China

6.China Agr Univ, Key Lab Smart Agr Syst, Minist Educ, Beijing, Peoples R China

7.Shandong Univ Sci & Technol, Coll Geomat, Qingdao, Peoples R China

8.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China

关键词： potato; plant nitrogen content; UAV; vegetation indices; morphological parameters

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： 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 (H), 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 H 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: R-2 = 0.79, RMSE=0.27, NRMSE=8.19%; S2:R-2 = 0.80, RMSE=0.27, NRMSE=8.11%; S3: R-2 = 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.