Spectroscopy and Spectral Analysis, Volume. 44, Issue 12, 3543(2024)

Deep Learning-Based Monitoring of Nutrient Content in Pear Trees

HUANG Lin-feng1, JIANG Xue-song1,2、*, JIA Zhi-cheng1, ZHOU Hong-ping1,2, ZHOU Lei1, and RONG Zi-fan1
Author Affiliations
  • 1Mechanical and Electronic Engineering College, Nanjing Forestry University, Nanjing 210037, China
  • 2Collaborative Innovation Center for Efficient Processing and Utilization of Forestry Resources,Nanjing 210037, China
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    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.

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    HUANG Lin-feng, JIANG Xue-song, JIA Zhi-cheng, ZHOU Hong-ping, ZHOU Lei, RONG Zi-fan. Deep Learning-Based Monitoring of Nutrient Content in Pear Trees[J]. Spectroscopy and Spectral Analysis, 2024, 44(12): 3543

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    Paper Information

    Received: Mar. 5, 2024

    Accepted: Jan. 16, 2025

    Published Online: Jan. 16, 2025

    The Author Email: JIANG Xue-song (xsjiang@njfu.edu.cn)

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

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