Estimating Nitrogen Content of Corn Based on Wavelet Energy Coefficient and BP Neural Network

Xiu, Lina; Zhang, Hui; Guo, Qiaozhen; Wang, Zhiheng; Liu, Xiangnan

doi:10.1109/icisce.2015.54

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…SVM was widely used in the field of remote sensing, and it was suitable for datasets with small sample sizes and high feature dimensions [48]. As one of the typical models of artificial neural networks, the BP neural network is composed of an input layer, a hidden layer, and an output layer, which can solve complicated nonlinear mapping problems [27,49]. In this study, all VIs were calculated by RGB, so there was a strong correlation between them.…”

Section: Discussionmentioning

confidence: 96%

Accuracy Comparison of Estimation on Cotton Leaf and Plant Nitrogen Content Based on UAV Digital Image under Different Nutrition Treatments

et al. 2023

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The rapid, accurate estimation of leaf nitrogen content (LNC) and plant nitrogen content (PNC) in cotton in a non-destructive way is of great significance to the nutrient management of cotton fields. The RGB images of cotton fields in Shihezi (China) were obtained by using a low-cost unmanned aerial vehicle (UAV) with a visible-light digital camera. Combined with the data of LNC and PNC in different growth stages, the correlation between N content and visible light vegetation indices (VIs) was analyzed, and then the Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BP), and stepwise multiple linear regression (SMLR) were used to develop N content estimation models at different growth stages. The accuracy of the estimation model was assessed by coefficient of determination (R2), root mean squared error (RMSE), and relative root mean square error (rRMSE), so as to determine the optimal estimated growth stage and the best model. The results showed that the correlation between VIs and LNC was stronger than that between PNC, and the estimation accuracy of different models decreased continuously with the development of growth stages, with higher estimation accuracy in the peak squaring stage. Among the four algorithms, the best accuracy (R2 = 0.9001, RMSE = 1.2309, rRMSE = 2.46% for model establishment, and R2 = 0.8782, RMSE = 1.3877, rRMSE = 2.82% for model validation) was obtained when applying RF at the peak squaring stage. The LNC model for whole growth stages could be used in the later growth stage due to its higher accuracy. The results of this study showed that there is a potential for using an affordable and non-destructive UAV-based digital system to produce predicted LNC content maps that are representative of the current field nitrogen status.

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Section: Discussionmentioning

confidence: 96%

Accuracy Comparison of Estimation on Cotton Leaf and Plant Nitrogen Content Based on UAV Digital Image under Different Nutrition Treatments

et al. 2023

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“…It can transform the spectral reflectance into new wavelet coefficients (Torrence & Compo, 1998) and reduce background noise by using wavelet functions. The study of Xiu et al (2015) used wavelet coefficients that exhibited the highest correlation coefficient with the N content of maize to construct a linear regression model and adopted wavelet coefficients with a higher correlation coefficient to build a multiple regression model. Also, using a good regression algorithm can improve the performance of the estimation model.…”

Section: Core Ideasmentioning

confidence: 99%

Predicting maize leaf area index by partial least square combined with wavelet transform

et al. 2022

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The leaf area index (LAI) is crucial for assessing and monitoring maize (Zea mays L.) vegetation status and photosynthetic ability. Predicting maize LAI by hyperspectral remote sensing technology is significant for managing agricultural production. In this study, three N rates (0, 120, and 240 kg N ha −1 ) and four drought stress treatments (60-70%, 45-55%, 30-40%, and 15-25% of field capacity), were imposed to provide the different environments for maize. The canopy spectral reflectance and LAI of maize were measured at the V6 and V12 stages. In this study, the main objectives were to investigate the performance of a new statistical method for monitoring LAI from canopy spectral reflectance. We used the canopy spectral reflectance to estimate the LAI and compared several methods of spectral analysis, including vegetation indices, wavelet functions, and the combination of continuous wavelet transform (CWT)-uninformative variable elimination (UVE)-partial least squares (PLS) (i.e., CWT-UVE-PLS), for spectral reflectance data analysis and model construction of maize LAI. Results showed that the model using the combination of db3-UVE-PLS achieved the best performance (with the highest coefficient of determination [R 2 ] and lowest RMSE for the calibration [R 2 = .979, RMSEC = .172] and validation [R 2 = .861, RMSEP = .533] datasets, respectively) in estimating the maize LAI. The CWT-UVE-PLS exhibited a considerable advantage in avoiding redundant or noise information interaction and achieving excellent correlations with maize LAI.

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“…[28] established the quantitative estimation models of N content rice canopy leaves based on adaptive differential optimization extreme learning machine, radial basis function (RBF) and particle swarm optimization BP. To estimate the N content of maize in natural environments rapidly and accurately, Xiu et al [29] proposed a method for measuring maize N content based on wavelet energy coe cient and back propagation neural network (BP). The method improved the accuracy of corn N content estimation when compared with the regression analysis model.…”

Section: Introductionmentioning

confidence: 99%

Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on spectral indices and wavelet neural network

Dong¹,

Liu²,

Ci³

et al. 2021

Preprint

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Background: Estimating nitrate nitrogen (NO3--N) content in petioles is one of the key approaches for monitoring nitrogen (N) nutrition in crops. Rapid, non-destructive, and accurate monitoring of great NO3--N content in cotton petioles under drip irrigation is of great significance.Methods: NO3--N content in cotton petioles under drip irrigation and the corresponding canopy spectral reflectance of cotton plants grown in experimental plots under various N application levels were analyzed. The correlations among ‘trilateral parameters’ and six vegetation indices, and NO3--N content in petioles were determined. A traditional regression model of NO3--N content in cotton petioles under drip irrigation was established, and a wavelet neural network (WNN) model with different index numbers was developed. The WNN model was verified using independent data, and compared with the random forest algorithm , radial basis function neural network and back propagation neural network.Results: Based on the analyses of ‘trilateral parameters’ and petiole NO3--N content, blue edge amplitude (Db) and blue edge area (SDb) of the blue edge parameters exhibited a strong positive correlation with petiole NO3--N content, and the correlation coefficients was 0.90. Among the blue edge parameters, the coefficient of determination (R2) of the Db polynomial regression equation and petiole NO3--N content was the highest (R2 = 0.89), while the root mean square error (RMSE) of the linear regression model was the lowest (RMSE = 1.04). R2 value of the traditional regression model developed using blue edge parameters and petiole NO3--N content significantly increased, while RMSE value decreased when compared with those of the red edge and yellow edge parameters. Analyses results of the vegetation index developed using original spectral reflectance data and the vegetation index developed using the first set of derivative spectral reflectance data and petiole NO3--N content, revealed that the first derivative vegetation index, normalized difference spectral index (ND705) exhibited a strong negative correlation, with a correlation coefficient of -0.90. The first derivative vegetation index, ND705 and petiole NO3--N content index regression equation had the highest coefficient of determination (R2 = 0.83), while the first derivative vegetation index, red edge model index (CIred-edge) and petiole NO3--N content linear regression equation had the lowest RMSE = 0.92. R2 value of the traditional regression equation for the first derivative vegetation index and petiole NO3--N content significantly increased, while the RMSE value decreased when compared with the original spectral vegetation index. After conducting correlation analyses and developing traditional regression models, Db and SDb of the blue edge parameters, and the first derivative vegetation index, ND705 and CIred-edge were used to develop a WNN model. The model based on blue edge parameters had R2 of 0.88, RMSE of 0.74g/L and mean absolute error (MAE) of 0.58 g/L, the R2 value was 8.6% higher than the R2 the first derivative vegetation index model, in which RMSE and MAE reduced by 18.7% and 20.5%, respectively. The model was tested using independent verification data, and which revealed that the R2 value of the model was 0.88, RMSE was 0.65g/L, and MAE was 0.47g/L based on the blue edge parameters, predicted value of WNN, and true value of the verification model, which was superior other models. The performance of the WNN model based on the blue edge parameters improved by 7.3%, and RMSE and MAE reduced by 25.2% and 30.9%, respectively when compared with those of the vegetation index model.Conclusion: The present study demonstrated that an inexpensive approach consisting of WNN algorithm and spectrum can be used to enhance the accuracy of NO3--N content estimation in cotton petioles under drip irrigation, which reflects their practical application potential.

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Estimating Nitrogen Content of Corn Based on Wavelet Energy Coefficient and BP Neural Network

Cited by 6 publications

References 19 publications

Accuracy Comparison of Estimation on Cotton Leaf and Plant Nitrogen Content Based on UAV Digital Image under Different Nutrition Treatments

Accuracy Comparison of Estimation on Cotton Leaf and Plant Nitrogen Content Based on UAV Digital Image under Different Nutrition Treatments

Predicting maize leaf area index by partial least square combined with wavelet transform

Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on spectral indices and wavelet neural network

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