Study on hyperspectral monitoring model of soil total nitrogen content based on fractional-order derivative

Yang, Chenbo; Feng, Meichen; Song, Lifang; Jing, Binghan; Xie, Yongkai; Wang, Chao; Yang, Wude; Xiao, Lujie; Zhang, Meijun; Song, Xingyu

doi:10.1016/j.compag.2022.107307

Cited by 22 publications

(7 citation statements)

References 44 publications

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“…The rapid monitoring of total flavonoids and total phenols in tartary buckwheat grains can be achieved, respectively. Yang et al [ 52 ] achieved the highest accuracy under FOD preprocessing when constructing the hyperspectral monitoring model of soil total nitrogen. This shows that when considering the effect of derivative preprocessing on the hyperspectral model, it is necessary to consider both IOD and FOD.…”

Section: Discussionmentioning

confidence: 99%

Study on Hyperspectral Monitoring Model of Total Flavonoids and Total Phenols in Tartary Buckwheat Grains

Yang

Song

Wei

et al. 2023

Foods

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Tartary buckwheat is a common functional food. Its grains are rich in flavonoids and phenols. The rapid measurement of flavonoids and phenols in buckwheat grains is of great significance in promoting the development of the buckwheat industry. This study, based on multiple scattering correction (MSC), standardized normal variate (SNV), reciprocal logarithm (Lg), first-order derivative (FD), second-order derivative (SD), and fractional-order derivative (FOD) preprocessing spectra, constructed hyperspectral monitoring models of total flavonoids content and total phenols content in tartary buckwheat grains. The results showed that SNV, Lg, FD, SD, and FOD preprocessing had different effects on the original spectral reflectance and that FOD can also reflect the change process from the original spectrum to the integer-order derivative spectrum. Compared with the original spectrum, MSC, SNV, Lg, FD, and SD transformation spectra can improve the correlation between spectral data and total flavonoids and total phenols in varying degrees, while the correlation between FOD spectra of different orders and total flavonoids and total phenols in grains was different. The monitoring models of total flavonoids and total phenols in grains based on MSC, SNV, Lg, FD, and SD transformation spectra achieved the best accuracy under SD and FD transformation, respectively. Therefore, this study further constructed monitoring models of total flavonoids and total phenols content in grains based on the FOD spectrum and achieved the best accuracy under 1.6 and 0.6 order derivative preprocessing, respectively. The R2c, RMSEc, R2v, RMSEv, and RPD were 0.8731, 0.1332, 0.8384, 0.1448, and 2.4475 for the total flavonoids model, and 0.8296, 0.2025, 0.6535, 0.1740, and 1.6713 for the total phenols model. The model can realize the rapid measurement of total flavonoids content and total phenols content in tartary buckwheat grains, respectively.

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Section: Discussionmentioning

confidence: 99%

Study on Hyperspectral Monitoring Model of Total Flavonoids and Total Phenols in Tartary Buckwheat Grains

Yang

Song

Wei

et al. 2023

Foods

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“…(2) The derivative processing method used in this study can be further optimized. The fractional derivative spectral data processing method has achieved good results in hyperspectral estimation of soil salinization ( Wang et al., 2018b ) and soil total nitrogen content ( Yang et al., 2022 ), However, the effect of improving the estimation accuracy of chlorophyll content in combination with dimensionality reduction algorithms needs to be further explored in the future. (3) In this study, we did not conduct year-round destructive experiments to directly establish the conversion relationship between LCC and SPAD values.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms

Tuerxun,

Zheng,

Wang

et al. 2023

Front. Plant Sci.

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The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2 = 0.99, RMSE=0.61) and measured dataset (R2 = 0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.

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“…Tavakoli et al preprocessed the visible and near-infrared spectra of soil using the dual-wavelength indices transformations and constructed a soil parameter prediction model based on stacking machine learning approaches [35]. Yang et al extracted 272 hyperspectral bands using uninformative variable elimination and constructed a soil total nitrogen prediction model based on partial least squares regression [36]. The sensitive bands of soil total nitrogen content were extracted based on the Pearson correlation coefficient, and the random forest model was used to build the inversion model [37].…”

Section: Rapid Detection Technology For Soil Nutrientsmentioning

confidence: 99%

Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients

Yuan,

Qi,

Huang

et al. 2024

Applied Sciences

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The intelligent diagnosis key technology of orchard nutrients provides a decision-making basis for precision fertilization, which has important research significance. This article reviewed the recent research literature, compared and analyzed existing technologies, and summarized solved and unresolved problems. It aimed to find breakthroughs to further improve the level of intelligent diagnosis key technology for orchard nutrients, and promote the implementation and application of the technology. Research had found that the current rapid nutrient detection technologies were mostly based on spectral data, with a focus on preprocessing algorithms and regression models. Hyperspectral technology shows good performance in predicting tree and soil nutrients due to its large number of characteristic variables. Meanwhile, preprocessing algorithms such as filtering, transformation, and feature band selection had also solved the problem of data redundancy. However, there were few studies for small and trace elements, and field applications. Laser breakdown-induced spectroscopy has good prospects for soil nutrient detection, as it can simultaneously detect multiple nutrients. There had been some studies on the technology for generating suitable nutrient standards for orchards in terms of soil and tree nutrients, but it requires a long and extensive experiment, which is time-consuming and laborious. A universal and rapid method needs to be studied to meet the construction needs of suitable nutrient standards for different varieties of fruit trees.

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Study on hyperspectral monitoring model of soil total nitrogen content based on fractional-order derivative

Cited by 22 publications

References 44 publications

Study on Hyperspectral Monitoring Model of Total Flavonoids and Total Phenols in Tartary Buckwheat Grains

Study on Hyperspectral Monitoring Model of Total Flavonoids and Total Phenols in Tartary Buckwheat Grains

Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms

Research Progress in Intelligent Diagnosis Key Technology for Orchard Nutrients

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