Estimation of Potato Plant Nitrogen Content Based on UAV Hyperspectral Imaging
FAN Yi-guang1, 3, 5, FENG Hai-kuan1, 2, 3*, LIU Yang1, 3, 4, LONG Hui-ling1, 3, YANG Gui-jun1, 3, QIAN Jian-guo5
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 100097, China
2. Nanjing Agricultural University, National Engineering and Technology Center for Information Agriculture，Nanjing 210095, China
3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
4. Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
5. School of Mapping and Geographical Science, Liaoning Technical University, Fuxin 123000, China
Abstract：Plant nitrogen content (PNC) is an essential indicator of crop growth and nitrogen nutrition status. Therefore, accurate and efficient access to PNC information is vital for dynamically monitoring potato growth and proper N fertilizer application. In this study, the UAV hyperspectral images were obtained at the budding stage, tuber formation stage, tuber growth stage, starch accumulation stage, and maturity stage of the potato. After preprocessing, the original canopy spectrum and first-order differential spectrum of five growth stages were extracted; Secondly, the correlation analysis was carried out between the extracted canopy spectrum and potato PNC, and the sensitive wavelength of PNC was screened out; Then, the texture and color of two image features of the hyperspectral image at the wavelength of the original spectral features of the canopy were extracted using the gray co-generation matrix and the 1st to 3rd-order color moments, respectively, and the extracted features were correlated with the potato PNC to filter out the top five image features with higher correlation; Finally, based on spectral features, image features, and map fusion features, potato PNC estimation models were established by using elastic network regression (ENR), Bayesian linear regression (BLR), and limit learning machine (ELM). The results showed that: (1) there are differences in the characteristic wavelengths of canopy spectra in the five growth stages of potatoes. Still, most of them were located in the visible region. (2) The correlation between the texture and color characteristics of the original spectral characteristic wavelength image of the canopy and PNC was high. The correlation from the budding stage to the starch store stage was significantly higher than that in the mature stage. (3) The estimation models of potato PNC based on a single spectral feature and a single image feature have a good effect from the budding stage to the starch accumulation stage but a poor effect at the maturity stage. (4) From the budding stage to the starch accumulation stage, the estimation effect of potato PNC based on the map fusion feature was significantly better than the single spectral feature and the single image feature. (5) In each growth period of potato, the PNC estimation models constructed by ENR based on the same variable were better, BLR was the second, and ELM was poor. Among them, the accuracy and stability of the PNC estimation models constructed by ENR with fusion characteristics as model variables were the best. The modeling R2 of five growth periods were 0.91, 0.75, 0.82, 0.77 and 0.69 respectively; RMSE were 0.24%, 0.31%, 0.26%, 0.22% and 0.29% respectively, and NRMSE were 6.59%, 9.79%, 9.58%, 7.87% and 11.03% respectively. This study can provide a fast and efficient technical tool for monitoring the nitrogen nutrition of potatoes.
樊意广，冯海宽，刘 杨，龙慧灵，杨贵军，钱建国. 基于无人机成像高光谱估算马铃薯植株氮含量[J]. 光谱学与光谱分析, 2023, 43(05): 1524-1531.
FAN Yi-guang, FENG Hai-kuan, LIU Yang, LONG Hui-ling, YANG Gui-jun, QIAN Jian-guo. Estimation of Potato Plant Nitrogen Content Based on UAV Hyperspectral Imaging. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1524-1531.
[1] He L, Song X, Feng W, et al. Remote Sensing of Environment, 2016, 174: 122.
[2] Xu X, Fan L, Li Z, et al. Remote Sensing, 2021, 13(3): 340.
[3] Zhu W, Li J, Li L, et al. International Journal of Agricultural and Biological Engineering, 2020, 13(2): 189.
[4] QIN Zhan-fei, CHANG Qing-rui, XIE Bao-ni, et al(秦占飞，常庆瑞，谢宝妮，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(23): 77.
[5] Fan L, Zhao J, Xu X, et al. Sensors, 2019, 19(13): 2898.
[6] WAGN Yu-na, LI Fen-ling, WANG Wei-dong, et al(王玉娜，李粉玲，王伟东，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(22): 31.
[7] LIU Yang, FENG Hai-kuan, SUN Qian, et al(刘 杨，冯海宽，孙 乾，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2020, 51(12): 202.
[8] Xu X, Li Z, Yang X, et al. Journal of Applied Remote Sensing, 2019, 13(3): 034505.
[9] Fu Y, Yang G, Li Z, et al. Computers and Electronics in Agriculture, 2020, 172: 105321.
[10] Yue J, Yang G, Li C, et al. Remote Sensing, 2017, 9(7): 708.
[11] Han Y, Qiu Y, Chen D, et al. Remote Sensing Letters, 2019, 10(3): 234.
[12] LIU Yang, FENG Hai-kuan, HUANG Jue, et al(刘 杨，冯海宽，黄 珏，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(23): 181.
[13] Fu Y，Yang G，Li Z, et al. Remote Sensing, 2020, 12(22): 3778.
[14] Yue J，Yang G, Tian Q，et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 150：226.
[15] Zhang H, Wang P, Dong Q, et al. Multiresolution and Information Processing, 2017, 15(3): 1750021.
