Estimation of foliar nitrogen of rubber trees using hyperspectral reflectance with feature bands

Li, Mao-Fen; Luo, Wei; Zhengzao, Cha

doi:10.1016/j.infrared.2019.103021

Cited by 12 publications

(3 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method is often used for quantitative analysis of spectra. Hyperspectral data has many bands, and there are problems of information redundancy and multicollinearity between the bands [35,36]. PLSR first uses principal component analysis in the modeling process to project spectral data onto a set of orthogonal factors that become latent variables and determine the optimal number of factors for the latent variables using internal crossvalidation.…”

Section: Linear Plsr Modelmentioning

confidence: 99%

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Tian

Zhu

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Soil salinization is a global ecological and environmental problem in arid and semi-arid areas that can be ameliorated via soil management, visible-near infrared-shortwave infrared (VNIR-SWIR) spectroscopy can be adapted to rapidly monitor soil salinity content. This study explored the potential of Grünwald–Letnikov fractional-order derivative (FOD), feature band selection methods, nonlinear partial least squares regression (PLSR), and four machine learning models to estimate the soil salinity content using VNIR-SWIR spectra. Ninety sample points were field scanned with VNIR-SWR and soil samples (0–20 cm) were obtained at the time of scanning. The samples points come from three zones representing different intensities of human interference (I, II, and III Zones) in Fukang, Xinjiang, China. Each zone contained thirty sample points. For modeling, we firstly adopted FOD (with intervals of 0.1 and range of 0–2) as a preprocessing method to analyze soil hyperspectral data. Then, four sets of spectral bands (R-FOD-FULL indicates full band range, R-FOD-CC5 bands that met a 0.05 significance test, R-FOD-CC1 bands that met a 0.01 significance test, and R-FOD-CC1-CARS represents CC1 combined with competitive adaptive reweighted sampling) were selected as spectral input variables to develop the estimation model. Finally, four machine learning models, namely, generalized regression neural network (GRNN), extreme learning machine (ELM), random forest (RF), and PLSR, to estimate soil salinity. Study results showed that (1) the heat map of correlation coefficient matrix between hyperspectral data and salinity indicated that FOD significantly improved the correlation. (2) The characteristic band variables extracted and used by R-FOD-CC1 were fewer in number, and redundancy between bands smaller than R-FOD-FULL and R-FOD-CC5, thus estimation accuracy of R-FOD-CC1 was higher than R-FOD-CC5 or R-FOD-FULL. A high prediction accuracy was achieved with a less complex calculation. (3) The GRNN model yielded the best salinity estimation in all three zones compared to ELM, BPNN, RF, and PLSR on the whole, whereas, the RF model had the worst estimation effect. The R-FOD-CC1-CARS-GRNN model yielded the best salinity estimation in I Zone with R2, RMSE and RPD of 0.7784, 1.8762, and 2.0568, respectively. The fractional order was 1.5 and estimation performance was great. The optimal model for predicting soil salinity in II and III Zone was, also, R-FOD-CC1-CARS-GRNN (R2 = 0.7912, RMSE = 3.4001, and RPD = 1.8985 in II Zone; R2 = 0.8192, RMSE = 6.6260, and RPD = 1.8190 in III Zone), with the fractional order of 1.7- and 1.6-, respectively, and the estimation performance were all fine. (4) The characteristic bands selected by the best model in I, II, and III Zones were 8, 9, and 11, respectively, which account for 0.45%, 0.51%, and 0.63%% of the full bands. This approach reduces the number of modeled band variables and simplifies the model structure.

show abstract

Section: Linear Plsr Modelmentioning

confidence: 99%

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Tian

Zhu

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…It is also unsuitable for the dynamic monitoring of crop growth, although the method has high measurement accuracy. With the continuous development of remote sensing technology, providing an effective method for estimation of the biophysical and biochemical factors of crops [14][15][16], crop growth can be monitored rapidly and nondestructively by sensing the spectral reflectance characteristics of the crop canopy [17,18]. Therefore, crop remote sensing has become an important research component in the field of precision agriculture.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images

Wang

Feng

et al. 2023

Agronomy

View full text Add to dashboard Cite

Nitrogen is one of the most important nutrients affecting the growth and fruit quality of walnut trees. Rapid and accurate monitoring of nitrogen concentration in the walnut canopy can provide a digital basis for its nutritional diagnosis and precision fertilization. Consequently, the main purpose of this study was to use Unmanned Aerial Vehicle (UAV) remote sensing technology to monitor the nitrogen concentration of walnut canopies. In this study, UAV multispectral images of the canopies of nine walnut orchards with different management levels in Wensu County, South Xinjiang, China, were collected during the fast-growing (20 May), sclerotization (25 June), and near-maturity (27 August) periods of walnut fruit, and canopy nitrogen concentration data for 180 individual plants were collected during the same periods. The validity of the information extracted via the outline canopy and simulated canopy methods was compared. The accuracy of nitrogen concentration inversion for three modeling methods, partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF), was analyzed; the effects of different combinations of variables on model accuracy were compared; and the spatial distribution of the nitrogen concentration in the walnut canopy was numerically mapped using the optimal model. The results showed that the accuracy of the model created using the single plant information extracted from the outlined canopy was better than that of the simulated canopy method, but the simulated canopy method was more efficient in extracting effective information from the single plant canopy than the outlined canopy. The simulated canopy method overcame the difficulty of mismatching the spectral information of individual plants extracted, by outlining the canopy in the original image for nitrogen distribution mapping with the spectral information of image elements in the original resolution image. The prediction accuracy of the RF model was better than that of the SVM and PLSR models; the prediction accuracy of the model using a combination of waveband texture information and vegetation index texture information was better than that of the single-source model. The coefficients of determination (R2) values of the RF prediction model built using the band texture information extracted via the simulated canopy method with the vegetation index texture information were in the range of 0.61–0.84, the root mean square error (RMSE) values were in the range of 0.27–0.43 g kg−1, and the relative analysis error (RPD) values were in the range of 1.58–2.20. This study shows that it is feasible to monitor the nitrogen concentration of walnut tree canopies using UAV multispectral remote sensing. This study provides a theoretical basis and methodological reference for the rapid monitoring of nutrients in fruit trees in southern Xinjiang.

show abstract

“…Hyperspectral imaging technology has been widely used in detecting and monitoring of agricultural production since it can obtain both the internal and external information of the sample rapidly and non-destructively [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] . It has been verified in previous research that the hyperspectral imaging technology is an efficient approach to detect the growth information of crops [8,9] , predict maturity and yield, track pests and diseases [11,12] , detect the soil nutrient [13] , meat quality [14] and pesticide residue [15][16][17] . At present, the technology is also applied to identify seed variety and detect the vigor, component and purity of the seeds [18][19][20][21][22][23][24][25] .…”

Section: Introductionmentioning

confidence: 99%

Identification of muskmelon seed variety using hyperspectral imaging technology combined with machine learning

Luo

Diao

et al. 2018

International Journal of Precision Agricultural Aviation

View full text Add to dashboard Cite

Liquid crystal tunable filter (LCTF) based on hyperspectral imaging technology combined with machine learning is developed to identify muskmelon seed variety rapidly and non-destructively. LCTF-based hyperspectral imaging system equipped with a cold ring LED source is constructed to acquire the reflectance spectra of the muskmelon seeds. Discriminating models based on support vector machine (SVM), linear discriminant analysis (LDA), and convolutional neural network (CNN) are then established to identify the seed variety with reflectance spectra as input. It is found that the LDA model achieved the highest classification accuracy of 100% for the test set while a relatively low value of 96% and 83% was obtained for the SVM and CNN model respectively. To improve classification accuracy of the model, data preprocessing (Savitzky-Golay smoothing and multiple scattering correction) and spectral feature extraction algorithm (successive projections algorithm and principal component analysis) were employed to treat the reflectance data. With these treatments, the classification accuracy of the test set was improved to the highest value of over 99% for the SVM model and 86% for the CNN model. The results showed that the LCTF-based hyperspectral imaging technology combined with machine learning was feasible to identify the muskmelon seed variety.

show abstract

Estimation of foliar nitrogen of rubber trees using hyperspectral reflectance with feature bands

Cited by 12 publications

References 69 publications

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Estimation of Salinity Content in Different Saline-Alkali Zones Based on Machine Learning Model Using FOD Pretreatment Method

Estimation of Nitrogen Concentration in Walnut Canopies in Southern Xinjiang Based on UAV Multispectral Images

Identification of muskmelon seed variety using hyperspectral imaging technology combined with machine learning

Contact Info

Product

Resources

About