Abstract
Nitrogen (N) is an important mineral element in apple production. Rapid estimation of apple tree N status is helpful for achieving precise N management. The objective of this work was to explore partial least squares (PLS) regression in dimensional reduction of spectral data and build the diagnostic model. The spectral reflectance data were collected from Fuji apple trees with 4 levels of N fertilizer treatment in the Loess Plateau in 2018 and 2019 using an ASD portable spectroradiometer, and leaf total N content was obtained at the same time. The raw spectra were pretreated using Savitzky-Golay (SG) smoothing and a combination of SG and first-order derivative (SG_FD) or second-order derivative (SG_SD). The samples were divided into a calibration dataset and a prediction dataset using SPXY. Based on 4 factors of PLS regression, including latent variables (LVs), X-loading, variable importance in projection (VIP) and regression coefficients (RC), the 6 methods (LVs, X-loading, VIP_01, VIP_02, RC_01 and RC_02) were derived and used for variable extraction, based on which PLS model and ELM model were established. The results indicated that the spectral data processed by SG_FD had the highest signal-to-noise ratio and was selected for subsequent analysis. The amounts of variables extracted by LVs, X-loading, VIP_01, VIP_02, RC_01 and RC_02 were 6, 11, 18, 305, 26 and 88, respectively. The method of extracting variables with an RC threshold based on the minimum RMSEP (RC_02) could effectively avoid the omission of effective information. The RC_02 method was recommended for related research which required accurate wavelength information as a variable. The variable extraction method based on LVs generated an ELM model with a simple structure. The prediction results showed that the ELM model outperformed the PLS model. The PLS(LVs)_ELM model was the best; R2P, RMSEP and RPD were 0.837, 2.393 and 2.220, respectively. Keywords: partial least square, variable extraction method, extreme learning machine, hyperspectral reflectance, apple tree, canopy nitrogen content DOI: 10.25165/j.ijabe.20211403.6157 Citation: Chen S M, Ma L H, Hu T T, Luo L H, He Q, Zhang S W. Nitrogen content diagnosis of apple trees canopies using hyperspectral reflectance combined with PLS variable extraction and extreme learning machine. Int J Agric & Biol Eng, 2021; 14(3): 181–188.
Highlights
Apple production is one of the important industries in terms of economic income in China
The aims of this study were (1) to extract variables of the canopy scale hyperspectral data from the spring-shoot-growing stage to the fruit enlargement stage using the 6 methods derived from 4 partial least squares (PLS)-related factors; (2) to propose a method to determine the threshold value of regression coefficient (RC) based on minimum root mean square error of prediction (RMSEP) and extract the key wavelengths; (3) to compare the performance of the PLS regression and extreme learning machine (ELM) model based on the variables from (1), so as to provide theoretical support for PLS assisted ELM to diagnose the apple tree canopies’ N content
Outliers were defined as points that fell outside the threshold values of 2.5 times the MEAN and 2.5 times the standard deviation (STD) denoted by the blue dotted line in Figure 2 below[14,31]
Summary
Apple production is one of the important industries in terms of economic income in China. The Loess Plateau is the largest advantageous apple production area in China and the world, accounting for 51.52% and 47.14% of the domestic planting area and fresh fruit production, respectively[1]. Previous studies have shown that nitrogen (N) is closely related to apple quality and yield[2]. The traditional laboratory method had poor timeliness in diagnosing N nutrition of plant. In the process of establishing diagnostic models of crop N nutrition, previous studies found that the sensitive bands of N content were greatly different under the influence of different crops[5,6,7]. With the development of artificial intelligence technology, CARS, UVE, GA, Random Frog and other methods were used to eliminate invalid variables and select sensitive wavelengths[8,9,10]
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