基于深度学习的梨树养分含量高光谱监测

doi:10.3964/j.issn.1000-0593(2024)12-3543-10

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Abstract To achieve wide-scale, accurate, and timely monitoring of pear nutrients during the growth and fruiting periods of pear trees and to further provide protection and adjustment strategies for pear fertilization management and pear fruit quality improvement, we used the ASD FieldSpec3 hyperspectral spectroscopy system to monitor the nutrient content of pear trees. The ASD FieldSpec3 hyperspectrometer was used to obtain leaf hyperspectral data during the fruit expansion and ripening periods of pear trees and to collect information on the leaf nitrogen, phosphorus, and potassium content. The effects of spectral preprocessing methods such as raw reflectance, first-order derivative transformation (FD), convolutional smoothing algorithm (SG), and standard normal variable transformation (SNV) on the fitting effect of the hyperspectral reflectance monitoring model were comparatively analyzed through the raw spectral curves. Principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and successive projection algorithm (SPA) are then used to select the characteristic bands of hyperspectral data. After that, partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), gradient boosted tree (GBDT), convolutional neural network (CNN), and deep forest (DF) algorithms were used to establish monitoring models based on the edge bands to screen the optimal hyperspectral monitoring models for the three nutrient elements, nitrogen, phosphorus, and potassium, in pear trees. The optimal modeling combination of nitrogen was the DF regression model of PCA eigenbands pre-processed by SG-SNV (R²=0.928 3, RMSE=0.238 1 g·kg^-1). The optimal modeling combination of phosphorus was the GBDT regression model of SPA eigenbands pre-processed by SG-SNV (R²=0.936 7, RMSE=0.043 1 g·kg^-1). The best modeling combination for potassium was the DF regression model (R²=0.954 4, RMSE=0.276 7 g·kg^-1) for the SG-SNV preprocessed PCA feature band. The monitoring model based on hyperspectral edge bands was well fitted (R²>0.9), which can realize the accurate monitoring of nitrogen, phosphorus, and potassium content in pear fruit during expansion and ripening.

Key words：Pear tree; Hyperspectral; Machine learning; Deep learning; Nutrient content

Received: 2024-03-05 Accepted: 2024-06-18

ZTFLH:

TP79

Corresponding Authors: JIANG Xue-song E-mail: xsjiang@njfu.edu.cn

Cite this article:

HUANG Lin-feng,JIANG Xue-song,JIA Zhi-cheng, et al. Deep Learning-Based Monitoring of Nutrient Content in Pear Trees[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(12): 3543-3552.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2024)12-3543-10 OR https://www.gpxygpfx.com/EN/Y2024/V44/I12/3543