Study of Spectral Response Characteristics of Oilseed Rape (Brassica napus) to Particulate Matters Based on Hyper-Spectral Technique

Lijuan, Kong; Yu, Hao; Piao, Zhaojia; Chen, Meichen; Dang, J. K.; Zhang, Lei; Sui, Yuan-yuan

doi:10.32604/phyton.2021.014190

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…This leads to an increase in lutein and a decrease in chlorophyll, resulting in a "blue shift" in the spectral features of the red edge and red valley [26,27]. The "red shift" in the red valley position and the "blue shift" in the green peak position during the maturation stage may be related to self-regulation or changes in the leaf structure of lettuce plants under cadmium stress [28,29]. Therefore, the red edge, red valley, and green peak can be used to discriminate the extent of cadmium pollution in lettuce plants.…”

Section: Analysis Of Spectral Characteristic Parameters In Lettuce Le...mentioning

confidence: 99%

Analysis of Cadmium Contamination in Lettuce (Lactuca sativa L.) Using Visible-Near Infrared Reflectance Spectroscopy

Zhou,

et al. 2023

Sensors

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In order to rapidly and accurately monitor cadmium contamination in lettuce and understand the growth conditions of lettuce under cadmium pollution, lettuce is used as the test material. Under different concentrations of cadmium stress and at different growth stages, relative chlorophyll content of lettuce leaves, the cadmium content in the leaves, and the visible-near infrared reflectance spectra are detected and analyzed. An inversion model of the cadmium content and relative chlorophyll content in the lettuce leaves is established. The results indicate that cadmium concentrations of 1 mg/kg and 5 mg/kg promote relative chlorophyll content, while concentrations of 10 mg/kg and 20 mg/kg inhibit relative chlorophyll content. The cadmium content in the leaves increases with increasing cadmium concentrations. Cadmium stress caused a “blue shift” in the red edge position only during the mature period, while the red valley position underwent a “blue shift” during the seedling and growth periods and a “red shift” during the mature period. The green peak position exhibited a “blue shift”. After model validation, it was found that the model constructed using the ratio of red edge area to yellow edge area and the normalized values of red edge area and yellow edge area effectively estimated the cadmium content in lettuce leaves. The model established using the normalized vegetation index of the red edge and the ratio of the peak green value to red shoulder amplitude can effectively estimate the relative chlorophyll content in lettuce leaves. This study demonstrates that the visible-near infrared spectroscopy technique holds great potential for monitoring cadmium contamination and estimating chlorophyll content in lettuce.

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Section: Analysis Of Spectral Characteristic Parameters In Lettuce Le...mentioning

confidence: 99%

Analysis of Cadmium Contamination in Lettuce (Lactuca sativa L.) Using Visible-Near Infrared Reflectance Spectroscopy

Zhou,

et al. 2023

Sensors

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“…Zhang et al constructed an estimation model for the content of different components of soil organic carbon in Australia [23]. Kong et al used a partial least squares method in combination with a variety of spectral absorption characteristics to construct an estimation model for soil organic carbon in the European Land Use/Cover Area frame Survey (LUCAS) [24]. Gruszczyńskiet al used CNN model to estimate K element in soil [25].…”

Section: B Problem Statementmentioning

confidence: 99%

Application of Hyperspectral Technology Combined With Bat Algorithm-AdaBoost Model in Field Soil Nutrient Prediction

et al. 2022

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This paper proposes a hyperspectral soil nutrient estimation method based on the bat algorithm (BA)-AdaBoost model. The spectral reflectance, the first derivative of the reflectance, and the reciprocal logarithm of the reflectance are analyzed based on the 800 field soil samples and their hyperspectral data collected. The first derivative of the reciprocal logarithm of the reflectance and the sensitive band was extracted using the correlation coefficient method, and the correlation of the content of soil organic matter, phosphorus, and potassium was solved. The BA is used to optimize the two core parameters of the AdaBoost model (i.e., the maximum number of iterations (n) and the weight reduction coefficient (v) of the weak learner), the classification and regression trees(CART) decision tree is selected as the weak regression learner of the model, and the coefficient of determination is used as parameter optimization. Based on the objective function value, a BA-AdaBoost model was constructed to estimate soil organic matter and phosphorus and potassium contents. The results show that the BA-AdaBoost combined model can better search for globally optimal parameters. The AdaBoost model optimized by BA significantly improved accuracy and reliability. Among the three elements, soil organic matter estimation accuracy is the highest, and the coefficient of determination and the root mean square error are 0.867 and 0.151g • kg -1 , respectively. Compared with the model before optimization, the model accuracy and reliability improved by 29.0% and 24.1%, respectively. The results indicate that hyperspectral technology combined with the BA-AdaBoost model has certain application prospects in field soil nutrient estimation.

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“…suffering from Orange Spotting, and found that the model had the highest prediction accuracy using the MSC data set. Kong et al (2021) found that when monitoring the chlorophyll content and net photosynthetic efficiency of Brassica napus affected by haze, MSC is the most optimized spectral pretreatment scheme. Izzuddin et al (2018) studied that continuous removal (CR) method and showed that it has good value for early disease monitoring in oil palm with basal stem rot (BSR).…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning

et al. 2022

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Powdery mildew has a negative impact on wheat growth and restricts yield formation. Therefore, accurate monitoring of the disease is of great significance for the prevention and control of powdery mildew to protect world food security. The canopy spectral reflectance was obtained using a ground feature hyperspectrometer during the flowering and filling periods of wheat, and then the Savitzky–Golay method was used to smooth the measured spectral data, and as original reflectivity (OR). Firstly, the OR was spectrally transformed using the mean centralization (MC), multivariate scattering correction (MSC), and standard normal variate transform (SNV) methods. Secondly, the feature bands of above four transformed spectral data were extracted through a combination of the Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA) algorithms. Finally, partial least square regression (PLSR), support vector regression (SVR), and random forest regression (RFR) were used to construct an optimal monitoring model for wheat powdery mildew disease index (mean disease index, mDI). The results showed that after Pearson correlation, two-band optimization combinations and machine learning method modeling comparisons, the comprehensive performance of the MC spectrum data was the best, and it was a better method for pretreating disease spectrum data. The transformed spectral data combined with the CARS–SPA algorithm was able to extract the characteristic bands more effectively. The number of bands screened was more than the number of bands extracted by the OR data, and the band positions were more evenly distributed. In comparison of different machine learning modeling methods, the RFR model performed the best (coefficient of determination, R2 = 0.741–0.852), while the SVR and PLSR models performed similarly (R2 = 0.733–0.836). Taken together, the estimation accuracy of spectral data transformation using the MC method combined with the RFR model (MC-RFR) was the highest, the model R2 was 0.849–0.852, and the root mean square error (RMSE) and the mean absolute error (MAE) ranged from 2.084 to 2.177 and 1.684 to 1.777, respectively. Compared with the OR combined with the RFR model (OR-RFR), the R2 increased by 14.39%, and the R2 of RMSE and MAE decreased by 23.9 and 27.87%. Also, the monitoring accuracy of flowering stage is better than that of grain filling stage, which is due to the relative stability of canopy structure in flowering stage. It can be seen that without changing the shape of the spectral curve, and that the use of MC to preprocess spectral data, the use of CARS and SPA algorithms to extract characteristic bands, and the use of RFR modeling methods to enhance the synergy between multiple variables, and the established model (MC-CARS-SPA-RFR) can better extract the covariant relationship between the canopy spectrum and the disease, thereby improving the monitoring accuracy of wheat powdery mildew. The research results of this study provide ideas and methods for realizing high-precision remote sensing monitoring of crop disease status.

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Study of Spectral Response Characteristics of Oilseed Rape (Brassica napus) to Particulate Matters Based on Hyper-Spectral Technique

Cited by 4 publications

References 22 publications

Analysis of Cadmium Contamination in Lettuce (Lactuca sativa L.) Using Visible-Near Infrared Reflectance Spectroscopy

Analysis of Cadmium Contamination in Lettuce (Lactuca sativa L.) Using Visible-Near Infrared Reflectance Spectroscopy

Application of Hyperspectral Technology Combined With Bat Algorithm-AdaBoost Model in Field Soil Nutrient Prediction

Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning

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