Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices

Yiguang Fan; Haikuan Feng; Xiuliang Jin; Jibo Yue; Yang Liu; Zhenhai Li; Zhihang Feng; Xiaoyu Song; Guijun Yang

doi:10.3389/fpls.2022.1012070

Estimation of the nitrogen content of potato plants based on morphological parameters and visible light vegetation indices

Front Plant Sci. 2022 Oct 18:13:1012070. doi: 10.3389/fpls.2022.1012070. eCollection 2022.

Authors

Yiguang Fan^{1

2}, Haikuan Feng^{1

3}, Xiuliang Jin⁴, Jibo Yue⁵, Yang Liu^{1

6}, Zhenhai Li⁷, Zhihang Feng⁸, Xiaoyu Song¹, Guijun Yang¹

Affiliations

¹ 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.
² School of Geographic, Liaoning Technical University, Fuxin, China.
³ College of Agriculture, Nanjing Agricultural University, Nanjing, China.
⁴ Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
⁵ College of Information and Management Science, Henan Agricultural University, Zhengzhou, China.
⁶ Key Lab of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing, China.
⁷ College of Geomatics, Shandong University of Science and Technology, Qingdao, China.
⁸ School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.

Abstract

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.

Keywords: UAV; morphological parameters; plant nitrogen content; potato; vegetation indices.