Estimation of Leaf Area Index of Soybean Based on Fractional Order Differentiation and Optimal Spectral Index

被引：0
|
作者：
Xiang Y. [1 ,2 ]
Wang X. [1 ,2 ]
An J. [1 ,2 ]
Tang Z. [1 ,2 ]
Li W. [1 ,2 ]
Shi H. [1 ,2 ]
机构：
[1] Key Laboratory of Agricultural Soil and Water Engineering in Arid Areas, Ministry of Education, Northwest A&F University, Shaanxi, Yangling
[2] College of Water Resources and Architectural Engineering, Northwest A&F University, Shaanxi, Yangling
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2023年 / 54卷 / 09期
关键词：
fractional order differentiation; hyperspectrum; leaf area index; optimal spectral index; soybean;
D O I：
10.6041/j.issn.1000-1298.2023.09.033
中图分类号：
学科分类号：
摘要：
Hyperspectral remote sensing crop growth monitoring technology, an essential instrument for developing contemporary precision agriculture, is characterized by non-destructiveness and real-time effectiveness. Taking leaf area index (LAI) of soybean at flowering stage under different levels of N application and mulching treatment as research object, the raw data for the hyperspectral reflectance of the soybean canopy were pretreated by using the 0-2 order differential transform processing (step 0. 5). Based on five sets of pretreatment reflectance data, the optimum spectral index with a high correlation to the LAI of soybean at the blooming stage was the input data. And the support vector machine (SVM), random forest (RF), and BP neural network optimized by genetic algorithm (GA-BP) were used to construct the soybean LAI prediction model. The results showed that compared with the integer order and the raw hyperspectral reflectance, the spectral indices built from the fractional order differential preprocessed hyperspectral reflectance correlated better with the soybean LAI. The corresponding bands of different orders of optimal spectral indices concentrated in the red-edge band. The correlation between the spectral index and soybean LAI was increased and then decreased as the differential order was increased, and the accuracy of the prediction model showed the same pattern. When the input data were the same for all three machine learning techniques, the model created by RF had the highest accuracy. A thorough analysis determined that the soybean LAI prediction model built by using RF had the highest accuracy of prediction when the input variable was the 1. 5-order differential optimal spectral index. The R2 of the model validation set was 0. 880, the RMSE was 0. 320 0 cmVcm2, the NRMSE was 10. 354% and the MRE was 9. 572%. The research result can help advance the development of precision agricultural production by offering theoretical references for enhancing the inversion accuracy of soybean LAI hyperspectral prediction models. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.
引用
收藏
页码：329 / 342
页数：13
相关论文
共 49 条
  • [21] FAVAF, COLOMBO R, BOCCHI S, Et al., Identification of hyperspectral vegetation indices for Mediterranean pasture characterization[J], International Journal of Applied Earth Observation and Geoinformation, 11, 4, pp. 233-243, (2009)
  • [22] CHEN Hongyan, ZHAO Gengxing, CHEN Jingchun, Et al., Remote sensing inversion of saline soil salinity based on modified vegetation index in estuary area of Yellow River[J], Transactions of the CSAE, 31, 5, pp. 107-114, (2015)
  • [23] SHAO Libing, CHEN Yiyun, XU Lu, Et al., Analysis on soil moisture content hyperspectral response and construction of estimation model based on fractional-order derivative[J], Journal of Geomatics, 47, pp. 131-136, (2022)
  • [24] TANG Z J, GUO J J, XIANG Y Z, Et al., Estimation of leaf area index and above-ground biomass of winter wheat based on optimal spectral index[J], Agronomy, 12, 7, pp. 1729-1746, (2022)
  • [25] SINHAL A, VERM A B., A distribution based approach of outlier removal for software effort data[J], International Journal of Computer Applications, 74, 20, pp. 24-28, (2013)
  • [26] LIU Shuang, YU Haiye, ZHANG Junhe, Et al., Study on inversion model of chlorophyll content in soybean leaf based on optimal spectral indices[J], Spectroscopy and Spectral Analysis, 41, 6, pp. 1912-1919, (2021)
  • [27] LIN Hui, LIANG Liang, ZHANG Lianpeng, Et al., Wheat leaf area index inversion with hyperspectral remote sensing based on support vector regression algorithm, Transactions of the CSAE, 29, 11, pp. 139-146, (2013)
  • [28] FANG Kuangnan, WU Jianbin, ZHU Jianping, Et al., A review of technologies on random forests [J], Journal of Statistics and Information, 26, 3, pp. 32-38, (2011)
  • [29] ZHANG Yin, ZHOU Mengran, Application of artificial neural network BP algorithm in near infrared spectroscopy, J Infrared, 11, pp. 1-4, (2006)
  • [30] YANG Lianbing, ZHENG Hongwei, LUO Geping, Et al., Retrieval of soil salinity content based on BP neural network optimized by genetic algorithm [J], Geography and Geo-Information Science, 37, 2, pp. 12-21, (2021)
12345