Spectral feature extraction and modeling of soil total nitrogen content based on NIR technology and wavelet packet analysis

Zheng, Lihua; Li, Minzan; An, Xiaofei; Pan, Luan; Sun, Hong

doi:10.1117/12.866220

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…The prediction accuracy of soil nitrogen content can be improved by different methods, namely, soil pretreatment, spectral reflectance data processing, feature band selection, and algorithm optimization [ 12 , 13 , 14 ]. However, the prediction accuracy of detecting soil nitrogen content with near infrared sensor is largely influenced by soil water content [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor

Xiao

Nie

et al. 2017

Sensors

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Nitrogen is one of the important indexes to evaluate the physiological and biochemical properties of soil. The level of soil nitrogen content influences the nutrient levels of crops directly. The near infrared sensor can be used to detect the soil nitrogen content rapidly, nondestructively, and conveniently. In order to investigate the effect of the different soil water content on soil nitrogen detection by near infrared sensor, the soil samples were dealt with different drying times and the corresponding water content was measured. The drying time was set from 1 h to 8 h, and every 1 h 90 samples (each nitrogen concentration of 10 samples) were detected. The spectral information of samples was obtained by near infrared sensor, meanwhile, the soil water content was calculated every 1 h. The prediction model of soil nitrogen content was established by two linear modeling methods, including partial least squares (PLS) and uninformative variable elimination (UVE). The experiment shows that the soil has the highest detection accuracy when the drying time is 3 h and the corresponding soil water content is 1.03%. The correlation coefficients of the calibration set are 0.9721 and 0.9656, and the correlation coefficients of the prediction set are 0.9712 and 0.9682, respectively. The prediction accuracy of both models is high, while the prediction effect of PLS model is better and more stable. The results indicate that the soil water content at 1.03% has the minimum influence on the detection of soil nitrogen content using a near infrared sensor while the detection accuracy is the highest and the time cost is the lowest, which is of great significance to develop a portable apparatus detecting nitrogen in the field accurately and rapidly.

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Section: Introductionmentioning

confidence: 99%

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor

Xiao

Nie

et al. 2017

Sensors

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“…Effective removal of background noise from soil spectra is necessary before modeling. Fourier transformation and wavelet packet transformation (WPT) are commonly used frequency domain filtering and denoising methods, which are widely used in many fields, such as structure detection and electronic signals [8][9][10]. In the decomposition and reconstruction of different layers of hyperspectral data, wavelet transformation can not only smooth the noise, but also maintain its original characteristics, which has a great advantage in hyperspectral processing [11,12].…”

Section: Introductionmentioning

confidence: 99%

Research on Hyperspectral Modeling of Total Iron Content in Soil Applying LSSVR and CNN Based on Shannon Entropy Wavelet Packet Transform

Liu,

Huo,

Zhou

et al. 2023

Remote Sensing

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The influence of some seemingly anomalous samples on modeling is often ignored in the quantitative prediction of soil composition modeling with hyperspectral data. Soil spectral transformation based on wavelet packet technology only performs pruning and threshold filtering based on experience. The feature bands selected by the Pearson correlation coefficient method often have high redundancy. To solve these problems, this paper carried out a study of the prediction of soil total iron composition based on a new method. First, regarding the problem of abnormal samples, the Monte Carlo method based on particle swarm optimization (PSO) is used to screen abnormal samples. Second, feature representation based on Shannon entropy is adopted for wavelet packet processing. The amount of information held by the wavelet packet node is used to decide whether to cut the node. Third, the feature bands selected based on the correlation coefficient and the competitive adaptive reweighted sampling (CARS) algorithm using the least squares support vector regression (LSSVR) are applied to the soil spectra before and after wavelet packet processing. Finally, the Fe content was calculated based on a 1D convolutional neural network (1D-CNN). The results show that: (1) The Monte Carlo method based on particle swarm optimization and modeling multiple times was able to handle the abnormal samples. (2) Based on the Shannon entropy wavelet packet transformation, simple operations could simultaneously preserve the spectral information while removing high-frequency noise from the spectrum, effectively improving the correlation between soil spectra and content. (3) The 1D-CNN with added residual blocks could also achieve better results in soil hyperspectral modeling with few samples.

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

Yang

Feng²,

Song³

et al. 2022

Computers and Electronics in Agriculture

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Spectral feature extraction and modeling of soil total nitrogen content based on NIR technology and wavelet packet analysis

Cited by 4 publications

References 9 publications

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor

Research on the Optimum Water Content of Detecting Soil Nitrogen Using Near Infrared Sensor

Research on Hyperspectral Modeling of Total Iron Content in Soil Applying LSSVR and CNN Based on Shannon Entropy Wavelet Packet Transform

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

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