Rapid detection of total nitrogen content in soil based on hyperspectral technology

Ma, Jingjing; Cheng, Junsheng; Wang, Jinghua; Pan, Ruoqian; He, Fang; Lei, Yan; Xiao, Jiang

doi:10.1016/j.inpa.2021.06.005

Cited by 12 publications

(9 citation statements)

References 26 publications

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“…(2) The IForest algorithm, as a global outlier detection tool, is capable of handling complex data with high speed and accuracy [44]. (3) Local spatial autocorrelation as a local outlier detection tool is capable of detecting spatial anomalies [45]. (4) The method integrates the discriminative results of global and local outliers for the final classification of the data, which avoids the possible misjudgement or omission of a single method and improves the reliability and stability of outlier identification.…”

Section: Discussionmentioning

confidence: 99%

“…Soil total nitrogen is the sum of the different forms of nitrogen in the soil, including organic and inorganic nitrogen [1], and is an important nutrient affecting grain yield and quality, with too much or too little nitrogen being detrimental to crop production [2,3]. Soil total nitrogen is usually determined by micro-Kjeldahl, semi-micro-Kjeldahl and micropotassium sulphate methods [4], and the results can provide an important basis for soil formulation and fertiliser application, help improve nitrogen fertiliser use and reduce pollution of agricultural surface water sources [5].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Approach for Soil Total Nitrogen Anomaly Detection Integrating Machine Learning and Spatial Statistics

Zheng,

Lan,

Zhangzhong

et al. 2023

Agronomy

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Soil total nitrogen is one of the most important basic indicators for fertiliser decision making, but tens of millions of soil total nitrogen sampling data have been accumulated, forming a huge database. In this large database, there is a large amount of anomalous data, which can interfere with data analysis, affect the construction of spatial interpolation and prediction models, and then affect the accuracy of nutrient management decisions. The traditional method of identifying soil total nitrogen anomalies based on boxplots suffers from the problems of not being able to identify local anomalies, which can easily lead to misclassification of soil total nitrogen data anomalies, and the detection efficiency is not high. We propose a method to identify soil total nitrogen outliers by combining the Isolation Forest algorithm and local spatial autocorrelation analysis, which can simultaneously detect global and local outliers from large amounts of data and combine organic matter as an auxiliary indicator in the spatial analysis to help judge local outliers. Finally, the results of global and local anomalies were combined to provide a comprehensive assessment of the soil nitrogen data, avoiding the misjudgement or omission of judgement that can occur when using a single method. Using 25,930 soil test data from Yunnan Province in 2009 as an example, we compared and analysed the typical boxplot method and the unsupervised OneClassSVM method and evaluated the performance of each method in terms of correct detection rate, false positive rate and false negative rate. The results show that the proposed method has a correct detection rate (TR) of 99.97%, a false positive rate (FPR) of 8.06% and a false negative rate (FNR) of 0.01% on the data, which shows high validity and accuracy; it is also comparable to the independent isolated forests (FNR = 4.76%), boxplot (FNR = 3.90%) and OneClassSVM (FNR = 4.77%), and the false negative rate is reduced by 4.75%, 3.89% and 4.76%, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Soil Total Nitrogen Anomaly Detection Integrating Machine Learning and Spatial Statistics

Zheng,

Lan,

Zhangzhong

et al. 2023

Agronomy

View full text Add to dashboard Cite

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“…Its learning rule is to use the gradient descent method, and constantly adjust the weights and thresholds of the network through backpropagation to minimize the sum of squared errors [33,34]. BP neural network has strong nonlinear mapping ability, flexible network structure, and fault tolerance ability, and its solution accuracy is difficult to reach by many conventional methods [35]. BP neural network is generally composed of the input layer, hidden layer, and output layer.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Detection and Analysis of Chili Pepper Root Rot by Hyperspectral Imaging Technology

Shao,

Ji,

Xuan

et al. 2024

Agronomy

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The objective is to develop a portable device capable of promptly identifying root rot in the field. This study employs hyperspectral imaging technology to detect root rot by analyzing spectral variations in chili pepper leaves during times of health, incubation, and disease under the stress of root rot. Two types of chili pepper seeds (Manshanhong and Shanjiao No. 4) were cultured until they had grown two to three pairs of true leaves. Subsequently, robust young plants were infected with Fusarium root rot fungi by the root-irrigation technique. The effective wavelength for discriminating between distinct stages was determined using the successive projections algorithm (SPA) after capturing hyperspectral images. The optimal index related to root rot between each normalized difference spectral index (NDSI) was obtained using the Pearson correlation coefficient. The early detection of root rot illness can be modeled using spectral information at effective wavelengths and in NDSI, together with the application of partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LSSVM), and back-propagation (BP) neural network technology. The SPA-BP model demonstrates outstanding predictive capabilities compared with other models, with a classification accuracy of 92.3% for the prediction set. However, employing SPA to acquire an excessive number of efficient wave-lengths is not advantageous for immediate detection in practical field scenarios. In contrast, the NDSI (R445, R433)-BP model uses only two wavelengths of spectral information, but the prediction accuracy can reach 89.7%, which is more suitable for rapid detection of root rot. This thesis can provide theoretical support for the early detection of chili root rot and technical support for the design of a portable root rot detector.

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“…Previous studies have shown that the preprocessing of spectral data can effectively reduce noise decomposition; therefore, extracting the sensitive bands of nitrogen spectra and constructing the model can improve the model accuracy. Ma et al [10] explored the possibility of using hyperspectral techniques for the detection of total soil nitrogen, SG smoothing, and MSC spectral data preprocessing, combined with five modeling methods-partial least squares (PLS), back propagation (BP) neural network, radial basis function (RBF) neural network, extreme learning machine (ELM), and SVR-to compare the errors of spectral analysis using chemical analysis results as a control. The results showed that all five models could be used for the detection of soil total nitrogen content, and the SG smoothing preprocessing model had a better detection ability compared with MSC, with an R-squared of 0.8767, and an RMSE of 1.302, among which the SVR model had the best accuracy, with an R-squared of 0.9121 and an RMSE of 0.7581.…”

Section: Introductionmentioning

confidence: 99%

Inversion Study of Nitrogen Content of Hyperspectral Apple Canopy Leaves Using Optimized Least Squares Support Vector Machine Approach

Hou,

Bai,

et al. 2024

Forests

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The rapid and accurate estimation of the nitrogen content of fruit trees helps to achieve a precise management of orchards. Hyperspectral data were collected from leaves of apple tree canopies at different fertility stages through field experiments to investigate the relationship between the nitrogen content and spectral reflectance of apple canopy leaves. Two different preprocessing methods, Savitzky–Golay (SG) smoothing and multiple scattering correction (MSC), were used to extract the feature bands by combining the successive projection method (SPA) and the competitive adaptive weighting algorithm–partial least squares (CARS-PLS). The reflectance values of the feature bands screened via these two methods were used as inputs to construct the multi-factor inversion models of apple canopy leaf nitrogen content based on the long- and short-term memory (LSTM) network, the support vector regression (SVR) and the Least Squares Support Vector Machine Regression (RIME-LSSVM). The study compared the ability of three algorithmic models to estimate leaf nitrogen content, and the results showed that the model constructed with the reflectance values of the characteristic bands screened by the CARS-PLS algorithm as inputs was more effective in predicting the nitrogen content of leaves. Furthermore, the accuracy of the model constructed using RIME-LSSVM was significantly higher than that of the model constructed using the long- and short-term memory network and support vector regression, in which the coefficient of determination of the test set (R-squared) is 0.964 and the root-mean-squared error (RMSE) is 0.052. Finally, the CARS-PLS algorithm combined with the RIME-LSSVM model has a higher prediction accuracy. The study demonstrated the feasibility and reliability of hyperspectral techniques for the estimation of nitrogen content of apple leaves in the Aksu region.

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Rapid detection of total nitrogen content in soil based on hyperspectral technology

Cited by 12 publications

References 26 publications

A Hybrid Approach for Soil Total Nitrogen Anomaly Detection Integrating Machine Learning and Spatial Statistics

A Hybrid Approach for Soil Total Nitrogen Anomaly Detection Integrating Machine Learning and Spatial Statistics

Detection and Analysis of Chili Pepper Root Rot by Hyperspectral Imaging Technology

Inversion Study of Nitrogen Content of Hyperspectral Apple Canopy Leaves Using Optimized Least Squares Support Vector Machine Approach

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