Applying Variable Selection Methods and Preprocessing Techniques to Hyperspectral Reflectance Data to Estimate Tea Cultivar Chlorophyll Content

Sonobe, Rei; Hirono, Yuhei

doi:10.3390/rs15010019

Cited by 3 publications

(3 citation statements)

References 72 publications

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“…In studies examining machine learning techniques, the Cubist algorithm has been shown globally to perform the best [37]. This excellent record of performance was also observed in the evaluation of vegetation properties based on reflectance data [23,24]. More recently still, deep-learning-based regression methods have gained in popularity for use in modeling complex relationships and making accurate predictions across various domains.…”

Section: Introductionmentioning

confidence: 86%

“…Recently, affordable, compact, fingertip-sized spectrometers (such as the C12880MA [Hamamatsu Photonics]) have been developed. These small devices can be attached to unmanned aerial vehicles for conducting measurements over large areas [23,24]. However, the measurements from these sensors contain various degrees of noise and may exhibit shifts.…”

Section: Introductionmentioning

confidence: 99%

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Carotenoid Content Estimation in Tea Leaves Using Noisy Reflectance Data

Sonobe,

Hirono

2023

Remote Sensing

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Quantifying carotenoid content in agriculture is essential for assessing crop nutritional value, improving crop quality, promoting human health, understanding plant stress responses, and facilitating breeding and genetic improvement efforts. Hyperspectral reflectance imaging is a nondestructive and rapid tool for estimating the carotenoid content. In spectrometer reflectance measurements, there are various sources of noise that can compromise the accuracy of carotenoid content estimations. Recently, various machine learning algorithms have been identified as robust against various types of noise, eliminating the need for denoising processes. Specifically, Cubist and the one-dimensional convolutional neural network (1D-CNN) have been used in evaluating vegetation properties based on reflectance data. We used regression models based on Cubist and 1D-CNN to estimate carotenoid content from reflectance data (the spectral resolution was resampled in 5 nm bands across the entire wavelength domain from 400 to 850 nm) with various degrees of Gaussian and spike noise added. The Cubist-based model was the most robust for this purpose: it achieved a ratio of performance to deviation of 1.41, a root mean square error of 1.11 µg/cm2, and a coefficient of determination (R2) of 0.496 when applied to reflectance data with a combination of Gaussian (mean: 0; variance: 0.04) and spike noise (density: 0.05; amplitude: 0.05).

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Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Carotenoid Content Estimation in Tea Leaves Using Noisy Reflectance Data

Sonobe,

Hirono

2023

Remote Sensing

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“…Figure 4e shows the spectral image after first-order derivation (D1) processing. This method highlights the rate of change in the peaks and facilitates peak detection and identification [27]. Figure 4f shows the spectral image after the second-order derivative (D2) processing, which highlights the curvature of the peaks and facilitates peak detection and identification [28].…”

Section: Changes In Fatty Acid Values Of Corn During Aging and Sample...mentioning

confidence: 99%

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology

Zhang,

Liu,

Zhou

et al. 2024

Molecules

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(1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination RP2 of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness.

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Spectral Quantitative Analysis and Research of Fusarium Head Blight Infection Degree in Wheat Canopy Visible Areas

et al. 2023

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Obtaining complete and consistent spectral images of wheat ears in the visible areas of in situ wheat canopies poses a significant challenge due to the varying growth posture of wheat. Nevertheless, detecting the presence and degree of wheat Fusarium head blight (FHB) in situ is critical for formulating measures that ensure stable grain production and supply while promoting green development in agriculture. In this study, a spectral quantitative analysis model was developed to evaluate the infection degree of FHB in an in situ wheat canopy’s visible areas. To achieve this, a spectral acquisition method was used to evaluate the infection degree of FHB in a wheat canopy’s visible areas. Hyperspectral images were utilized to obtain spectral data from healthy and mildly, moderately, and severely infected wheat ear canopies. The spectral data were preprocessed, and characteristic wavelengths were extracted using twelve types of spectral preprocessing methods and four types of characteristic wavelength extraction methods. Subsequently, sixty-five spectral quantitative prediction models for the infection degree of FHB in the in situ wheat canopy visible areas were established using the PLSR method, based on the original spectral data, preprocessed spectral data, original spectral characteristic wavelengths extracted data, and preprocessed spectral characteristic wavelengths extracted data. Comparative analysis of the models indicated that the MMS + CARS + PLSR model exhibited the best prediction effect and could serve as the spectral quantitative analysis model for the evaluation of the infection degree of FHB in an in situ wheat canopy’s visible areas. The model extracted thirty-five characteristic wavelengths, with a modeling set coefficient of determination (R2) of 0.9490 and a root-mean-square error (RMSE) of 0.2384. The testing set of the coefficient of determination (R2) was 0.9312, with a root-mean-square error (RMSE) of 0.2588. The model can facilitate the spectral quantitative analysis of the infection degree of FHB in the in situ wheat canopy visible areas, thereby aiding in the implementation of China’s targeted poverty alleviation and agricultural power strategy.

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Applying Variable Selection Methods and Preprocessing Techniques to Hyperspectral Reflectance Data to Estimate Tea Cultivar Chlorophyll Content

Cited by 3 publications

References 72 publications

Carotenoid Content Estimation in Tea Leaves Using Noisy Reflectance Data

Carotenoid Content Estimation in Tea Leaves Using Noisy Reflectance Data

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology

Spectral Quantitative Analysis and Research of Fusarium Head Blight Infection Degree in Wheat Canopy Visible Areas

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