Development of a predictive tool for rapid assessment of soil total nitrogen in wheat-corn double cropping system with hyperspectral data

Song, Xuehang; Gao, Yongxiang; Liu, Zhiguang; Wan, Yongshan; Yu, Xiaojing; Liu, Wenlong; Li, Lei

doi:10.1080/26395940.2019.1679041

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…For the problem of detecting anomalies in TN data, we proposed a method for identifying TN outliers by combining the isolated forest algorithm and the local spatial autocorrelation analysis method and identified the outliers of TN data in Yunnan Province in 2009 and reached the following conclusions: (1) The overall situation of TN content in Yunnan Province is good, and at the same time, TN is positively correlated with SOM, PH, AP and AK relationship, and had a strong correlation with soil organic matter (R = 0.74). (2) The proposed method has high detection validity and accuracy in detecting TN anomalies, and in the detection effect of 2009 TN data in Yunnan Province, it showed that the TR could reach 99.97%, and the FNR was 0.01%. (3) Comparing the proposed method with the independent isolated forest (FNR = 4.76%), boxplot (FNR = 3.90%), OneClassSVM (FNR = 4.77%) and the combination method of boxplot, OneClassSVM combined with spatial analysis, respectively, the results show that the proposed method (FNR = 0.01%) is better than the single method and other combination methods.…”

Section: Discussionmentioning

confidence: 86%

“…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%

“…This study compared and analysed the method with other commonly used methods using soil test data from Yunnan Province in 2009 as an example and evaluated the performance of each method in terms of correct detection rate, false positive rate and false negative rate. The objectives of this study were (1) to analyse the data characteristics of the five basic soil nutrient test data in Yunnan Province in 2009, (2) to evaluate the performance of the proposed TN outlier identification model, (3) to compare the accuracy of different methods for identifying TN outliers and (4) to analyse the characteristics of soil total nitrogen distribution in Yunnan Province. The proposed method provides a new idea and method for identifying TN outliers and supports soil nutrient analysis and assessment.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

show abstract

“…Additionally, decisions regarding fertility, regions of improvement, potential crop yields etc. can be taken with technological evidence [15].…”

Section: Resultsmentioning

confidence: 99%

Unsupervised Unmixing and Segmentation of Hyper Spectral Images Accounting for Soil Fertility

Lavanya

Subalakshmi

Thirugnanam

et al. 2022

SCPE

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A crucial component of precision agriculture is the capability to assess the fertility of soil by looking at the precise distribution and composition of its different constituents. This study aims to investigate how different machine learning models may be used to assess soil fertility using hyperspectral pictures. The development of images using a random mixing of different soil components is the first phase, and the hyper spectral bands utilized to create the images are not used again during the analysis procedure. The resulting end members are then acquired by applying the NFINDR algorithm to the process of spectral unmixing this image. The comparison between these end members and the band values of the known elements is then quantified., i.e. it is represented as a graph of band values obtained through spectral unmixing. Finally we quantify the similarities between both graphs and proceed towards the classification of the hyper spectral image as fertile or infertile. In order to classify the hyper spectral image as fertile or infertile, we quantify the similarities between the two graphs. Clustering and picture segmentation algorithms have been devised to help with this process, and a comparison is then made to show which techniques are the most effective.

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“…Sweet corn is a widely cultivated food crop because it is rich Sensors 2022, 22, 1822 2 of 17 in vitamins, amino acids and so on, making it more and more popular [13,14]. Nitrogen, phosphorus and potassium in sweet corn are important nutrients for its growth, which have great influence on its growth, yield and quality [15][16][17]. Because the traditional diagnosis of maize nutrient elements adopts chemical methods, a large number of corn samples need to be picked, and complicated chemical testing is required, which takes a long time and will cause damage to the plant [18].…”

Section: Introductionmentioning

confidence: 99%

Spectral Diagnostic Model for Agricultural Robot System Based on Binary Wavelet Algorithm

Tang

Gao

et al. 2022

Sensors

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The application of agricultural robots can liberate labor. The improvement of robot sensing systems is the premise of making it work. At present, more research is being conducted on weeding and harvesting systems of field robot, but less research is being conducted on crop disease and insect pest perception, nutritional element diagnosis and precision fertilizer spraying systems. In this study, the effects of the nitrogen application rate on the absorption and accumulation of nitrogen, phosphorus and potassium in sweet maize were determined. Firstly, linear, parabolic, exponential and logarithmic diagnostic models of nitrogen, phosphorus and potassium contents were constructed by spectral characteristic variables. Secondly, the partial least squares regression and neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium contents were constructed by the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition. The results show that the neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium content based on the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition is better. The R2, MRE and NRMSE of nn of nitrogen, phosphorus and potassium were 0.974, 1.65% and 0.0198; 0.969, 9.02% and 0.1041; and 0.821, 2.16% and 0.0301, respectively. The model can provide growth monitoring for sweet corn and a perception model for the nutrient element perception system of an agricultural robot, while making preliminary preparations for the realization of intelligent and accurate field fertilization.

show abstract

Development of a predictive tool for rapid assessment of soil total nitrogen in wheat-corn double cropping system with hyperspectral data

Cited by 5 publications

References 25 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

Unsupervised Unmixing and Segmentation of Hyper Spectral Images Accounting for Soil Fertility

Spectral Diagnostic Model for Agricultural Robot System Based on Binary Wavelet Algorithm

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