A Robust Fuzzy Neural Network Model for Soil Lead Estimation from Spectral Features

Goodarzi, Rohollah; Mokhtarzade, Mehdi; Zoej, Mohammad Javad Valadan

doi:10.3390/rs70708416

Cited by 19 publications

(11 citation statements)

References 28 publications

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“…Goodarzi et al [11] performed statistical tests such as Pearson and partial correlation analysis and used the past histories (results of previous literatures) to choose the appropriate spectral range and the most effective spectral regions. They applied Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN) [12] and Fuzzy Neural Network (FNN) [13] models on these selected spectral regions for lead (Pb) concentration estimation and proved that FNN model was more powerful than the other models.…”

Section: Fan Et Al [9] Employed Three Wavelength Selection Methods Wh...mentioning

confidence: 99%

A Sensitive Wavebands Identification System for Smart Farming

Kavitha¹,

Sujaritha²

2022

Computer Systems Science and Engineering

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Sensing the content of macronutrients in the agricultural soil is an essential task in precision agriculture. It helps the farmers in the optimal use of fertilizers. It reduces the cost of food production and also the negative environmental impacts on atmosphere and water bodies due to indiscriminate dosage of fertilizers. The traditional chemical-based laboratory soil analysis methods do not serve the purpose as they are hardly suitable for site specific soil management. Moreover, the spectral range used in the chemical-based laboratory soil analysis may be of 350-2500 nm, which leads to redundancy and confusion. Developing sensors based on the discovery of spectral wavebands that respond to soil macronutrient concentrations, on the other hand, is an innovative and successful technology since the results are dependable and timely. The goal of this article is to use a supervised neuro-fuzzy based dimensionality reduction approach in the sensor development process to determine sensitive wavebands of soil macronutrients. Accordingly, the spectral signatures of the soil are collected in an outdoor environment and mapped with its macronutrient concentrations. In this spectral analysis, the spectral reflectance of 424 wavelengths has been obtained and these wavelengths are evaluated through combined and individual modes as well. Appropriate wavelengths are selected in each case by minimizing the fuzzy reflectance assessment index. The effectiveness of these selected wavelengths in each mode is validated by modeling the relation between the reduced reflectance space and each macronutrient concentration using Partial Least Squares Multi Variable Regression (PLS-MVR) method. Set of optimal wavebands are identified and the results are compared with the existing systems.

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Section: Fan Et Al [9] Employed Three Wavelength Selection Methods Wh...mentioning

confidence: 99%

A Sensitive Wavebands Identification System for Smart Farming

Kavitha¹,

Sujaritha²

2022

Computer Systems Science and Engineering

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“…In general, the application of advanced MLs depends greatly on the related theory or technique. For example, fuzzy set concepts [62] are usually attached to basic MLs to make the decision space in categorizing more flexible (fuzzy random forest [63]; fuzzy nonlinear proximal SVM [64]; fuzzy-NN [65]). Markov-systems-based techniques also belong to the leading trend of increasing the classification accuracy (Markov-random-field-based SVM [57]; MLP-Markov chain models [66]).…”

Section: Advanced Extension Of the Mlsmentioning

confidence: 99%

The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy

Boonprong

Cao

Wei

et al. 2018

IJGI

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Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robustness against noise. However, only a few comparative studies that may help investigate this robustness have been reported. An important contribution, going beyond previous studies, is that we perform the analyses by employing the most well-known and broadly implemented packages of the three classifiers and control their settings to represent users’ actual applications. This facilitates an understanding of the extent to which the noise types and levels in remotely sensed data impact classification accuracy using ML classifiers. By using those implementations, we classified the land cover data from a satellite image that was separately afflicted by seven-level zero-mean Gaussian, salt–pepper, and speckle noise. The modeling data and features were strictly controlled. Finally, we discussed how each noise type affects the accuracy obtained from each classifier and the robustness of the classifiers to noise in the data. This may enhance our understanding of the relationship between noises, the supervised ML classifiers, and remotely sensed data.

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“…Accurate estimation of the total iron content in soil (TICS) is helpful for agronomists to assess soil conditions, which is also the key to ensure the healthy growth of crops. Therefore, rapid and precise prediction of TICS has an important practical value for precision agriculture [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network

et al. 2021

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Background The estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops. Methods In this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and back propagation (BP) neural network model. Several data preprocessing methods of first derivative (FD), wavelet packet transform (WPT), and HA were conducted to improve the correlation between the soil spectra and TICS. The principal component analysis (PCA) was exploited to obtained three kinds of characteristic variables (FD, WPT-FD, and WPT-FD-HA) for TICS estimation. Furthermore, the estimated accuracy of three BP models based on these variables was compared. Results The results showed that the BP models of different soil types based on WPT-FD-HA had better estimation accuracy, with the highest R2 value of 0.95, and the RMSE of 0.68 for the loessial soil. It was proved that the characteristic variable obtained by harmonic decomposition improved the validity of the input variables and the estimation accuracy of the TICS models. Meanwhile, it was identified that the WPT-FD-HA-BP model can not only estimate the total iron content of a single soil type with high accuracy but also demonstrate a good effect on the estimation of TICS of mixed soil. Conclusion The HA method and BP neural network combined with WPT and FD have great potential in TICS estimation under the conditions of single soil and mixed soil. This method can be expected to be applied to the prediction of crop biochemical parameters.

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A Robust Fuzzy Neural Network Model for Soil Lead Estimation from Spectral Features

Cited by 19 publications

References 28 publications

A Sensitive Wavebands Identification System for Smart Farming

A Sensitive Wavebands Identification System for Smart Farming

The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy

Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network

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