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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

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A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging.

Xin Zhou¹, Chunjiang Zhao², Jun Sun¹

¹School of Electrical and Information Engineering of Jiangsu University, Zhenjiang 212013, China.

|December 31, 2022

Summary

This study introduces a deep learning method using wavelet transform (WT) and stacked denoising autoencoder (SDAE) for detecting lead (Pb) in oilseed rape leaves via fluorescence hyperspectral technology.

Keywords:

Fluorescence hyperspectral imaging Heavy metal lead Nondestructive testing Oilseed rape Stacked denoising autoencoder Wavelet transform

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Area of Science:

Agricultural Science
Spectroscopy
Machine Learning

Background:

Heavy metal contamination, particularly lead (Pb), poses significant risks to crop health and food safety.
Accurate and efficient detection methods for heavy metals in plants are crucial for agricultural monitoring and risk assessment.

Purpose of the Study:

To develop and validate a deep learning approach for detecting lead (Pb) in oilseed rape leaves using fluorescence hyperspectral data.
To optimize feature extraction by combining wavelet transform (WT) and stacked denoising autoencoder (SDAE).

Main Methods:

Utilized standard normalized variable (SNV) for fluorescence spectral data preprocessing.
Applied wavelet transform (WT) for optimal decomposition of spectral data.
Employed stacked denoising autoencoder (SDAE) for deep feature learning.
Developed a support vector machine regression (SVR) model for prediction.

Main Results:

The optimal wavelet basis function (sym7) and decomposition layers were identified.
The developed deep learning model achieved high prediction accuracy (Rₚ² = 0.9388, RPD = 3.275) for lead (Pb) detection.
Demonstrated the effectiveness of SNV preprocessing and WT-SDAE feature extraction.

Conclusions:

Fluorescence hyperspectral technology combined with deep learning algorithms shows great potential for detecting heavy metals in plants.
The proposed WT-SDAE method offers a robust approach for quantitative analysis of lead contamination in oilseed rape.