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| Hyperspectral Inversion of Soil Organic Matter in Jujube Orchard
in Southern Xinjiang Using CARS-BPNN |
| CAI Hai-hui1, ZHOU Ling2, SHI Zhou3, JI Wen-jun4, LUO De-fang1, PENG Jie1, FENG Chun-hui5* |
1. College of Agriculture, Tarim University, Alar 843300, China
2. College of Mechanical and Electronic Engineering, Tarim University, Alar 843300, China
3. Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310058, China
4. College of Land Science and Technology, China Agricultural University, Beijing 100083, China
5. College of Horticulture and Forestry, Tarim University, Alar 843300, China
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Abstract The soil organic content is the main basis for developing soil fertilization programs in jujube orchards. A reasonable fertilization program is of great significance for improving the quality of jujube, reducing farmers' investment and increasing the output of jujube orchards . However, it is time-consuming and resource-intensive to obtain SOM content of jujube orchards using the traditional method, which does not meet the needs of precise fertilization management in jujube orchards. At the same time, the hyperspectral detection of soil organic matter is an effective alternative method. 158 soil samples are collected by grid distribution method, and the indoor hyperspectral data and SOM content of air-dried soil samples are determined. The 400~2 400 nm full waveband (R) and the datasets selected by three data reduction algorithms of competitive adaptive weighting algorithm (CARS), successive projection algorithm (SPA) and particle swarm optimization algorithm (PSO) are combined with three modeling methods, which are partial least squares regression (PLSR), back propagation neural network (BPNN) and convolutional neural network (CNN) to construct 12 combined inversion models of SOM content of jujube orchards. Moreover, the optimal spectral inversion model of SOM content of jujube orchards was selected by comparing the accuracy evaluation index and training time of the models. The results show that (1) CARS, SPA and PSO can all compress the spectral data to less than 10% of the original data, and the number of screened wavelengths is reduced from the original 2001 variables to 98, 156 and 102, respectively. The validation set RPD of the dimensionality reduction combined model are all greater than 1.50, and all of them can achieve the inversion of the SOM content of jujube orchards. Compared with the R combined model, the dimensionality reduction combined model can save at least 30% of time cost, especially the combined model constructed with BPNN and CNN can save 90% of the training time, and the model has stronger stability and better model effect. (2)The validation set of the CARS dataset to construct the combined model has R2 greater than 0.85 and RPD greater than 2.50, which is the best among the three-dimensionality reduction algorithms; the validation effect of the combined model of the PSO dataset is slightly lower than that of the CARS dataset, but better than that of the R dataset, with R2 greater than 0.80 and RPD greater than 2.00; the validation effect of the SPA dataset to construct the combined model is lower than that of the R dataset The validation effect of the SPA dataset is lower than that of the R dataset, and the effect is the worst among the three-dimensionality reduction algorithms. (3) Both BPNN and CNN methods outperformed the PLSR model in terms of inversion model validation, while the BPNN model out performed the CNN model in terms of model training time and model validation effect, and its validation effect combined with the CARS dataset is optimal with R2 of 0.91, PRD of 3.34, nRMSE% of 11.93, and training time of 58.00 s. The model can detect the SOM content of the jujube orchards rapidly. The CARS-BPNN model is the optimal model for the inversion of SOM in jujube orchards in South Xinjiang, and the results of the study can provide a reference for the rapid detection of soil nutrients and formulation of fertilization plan in jujube orchards in South Xinjiang.
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Received: 2021-12-08
Accepted: 2022-12-14
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Corresponding Authors:
FENG Chun-hui
E-mail: chunhui.f@outlook.com
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[1] YU Lei, HONG Yong-sheng, ZHOU Yong, et al(于 雷, 洪永胜, 周 勇, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(13): 95.
[2] Reda R, Saffaj T, Ilham B, et al. Chemometrics and Intelligent Laboratory Systems, 2019, 195: 103873.
[3] Nadler B, Coifman R R. Journal of Chemometrics, 2005, 19(2): 107.
[4] Yun Y H, Li H D, Deng B C, et al. TrAC Trends in Analytical Chemistry, 2019, 113: 102.
[5] NIU Fang-peng, LI Xin-guo, MAMATTURSUN Eziz, et al(牛芳鹏, 李新国, 麦麦提吐尔逊·艾则孜, 等). Journal of Zhejiang University（Agriculture and Life Sciences）[浙江大学学报（农业与生命科学版)], 2021, 47(5): 673.
[6] Huan K, Chen X, Song X, et al. Infrared Physics & Technology, 2021, 119: 103937.
[7] Padarian J, Minasny B, McBratney A B. Geoderma Regional, 2019, 16: e00198.
[8] YU Wu, JIA Xiao-lin, CHEN Song-chao, et al(喻 武, 贾晓琳, 陈颂超, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2018, 38(4): 1076.
[9] Liu J, Dong Z, Xia J, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2021, 258: 119823.
[10] Chen Y, Li L, Whiting M, et al. International Journal of Applied Earth Observation and Geoinformation, 2021, 104: 102550.
[11] Pullanagari R R, Dehghan-Shoar M, Yule I J, et al. Remote Sensing of Environment, 2021, 257: 112353.
[12] NIU Hai-qing, ZHENG Wen-jian, LEI Chao-ping, et al(牛海清, 郑文坚, 雷超平, 等). High Voltage Engineering(高电压技术). 2018, 44(5): 1557.
[13] TANG Hai-tao, MENG Xiang-tian, SU Xun-xin, et al(唐海涛, 孟祥添, 苏循新, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2021, 37(2): 105.
[14] SHI Zhou, WANG Qian-long, PENG Jie, et al(史 舟, 王乾龙, 彭 杰, 等). Scientia Sinica(Terrae)(中国科学: 地球科学), 2014, 44(5): 978.
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