Prediction of winter wheat leaf chlorophyll content based on <scp>VIS</scp>/<scp>NIR</scp> spectroscopy using <scp>ANN</scp> and <scp>PLSR</scp>

Sharabiani, Vali Rasooli; Soltani-Nazarloo, Araz; Taghinezhad, Ebrahim; Veza, Ibham; Szumny, Antoni; Figiel, Adam

doi:10.1002/fsn3.3071

Cited by 14 publications

(7 citation statements)

References 60 publications

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“…It is an algorithmic model that can judge the problems of the human brain from the perspective of information processing. The ability to approximate and the information is used to complete the simplest abstract calculations [31]. The neural network is composed of an input layer, an output layer, and one or more hidden layers, which are given corresponding weights through the connection between neurons, and the weights are continuously adjusted through training and learning algorithms to ultimately reach an optimum.…”

Section: Modeling Methodsmentioning

confidence: 99%

Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index

Gao,

Zhang,

et al. 2024

Applied Sciences

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Chlorophyll content in wheat leaves reflects its growth and nutritional status, which can be used as a health index for field management. In order to evaluate the potential of hyperspectral data to estimate the chlorophyll content in wheat leaves, this study focused on the leaves of wheat at the flag-picking stage, flowering stage, grain-filling stage, and maturity stage. Based on the framework of five vegetation indexes, the spectral index was constructed by using the combination of 400–1000 nm bands. The correlation between the constructed spectral index and the measured chlorophyll value was analyzed, and the optimal spectral index was screened using the correlation coefficient. Based on the optimal spectral index, polynomial regression, random forest, decision tree, and artificial neural network were used to establish the estimation model for chlorophyll value, and the optimal model for estimating the chlorophyll value of wheat leaves was selected through model evaluation. The results showed that the five optimal spectral indices at the four growth stages were primarily composed of the red band, red edge band, and near-infrared band. The five optimal spectral indices during the grain-filling stage had the highest correlation with the chlorophyll value, and the absolute value of the correlation coefficient was greater than 0.73. The accuracy of the estimation model established in the four growth stages was different, with the estimation accuracy of the flag stage being the best, showing an R2 and RMSE of 0.79 and 2.63, respectively. These results indicate that hyperspectral data are suitable for estimating the chlorophyll value of wheat leaves, and the polynomial regression model of the flag-picking period can be used as the optimal model for estimating the chlorophyll value of wheat leaves.

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Section: Modeling Methodsmentioning

confidence: 99%

Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index

Gao,

Zhang,

et al. 2024

Applied Sciences

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“…In our case, we used GA-ANN, k-Best, and PC-ANN, where the variables were reduced from the original full spectra to some featured wavelength data. Recently, in 2023, Rasooli Sharabiani et al [ 34 ] used ANN with samples of winter wheat leaf for evaluation of chlorophyll content based on VIS/NIR spectroscopy using PLSR and ANN, where 120 samples were for the training set and the left was for the test set. The models resulted in the most accurate predictions, with a correlation coefficient of 0.92 and 0.97, along with a root mean square error of 0.9131 and 0.7305, respectively.…”

Section: Discussionmentioning

confidence: 99%

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Jongyingcharoen,

Howimanporn,

Sitorus

et al. 2024

Polymers

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Classification of the crosslink density level of para rubber medical gloves by using near-infrared spectral data combined with machine learning is the first time reported in this paper. The spectra of medical glove samples with different crosslink densities acquired by an ultra-compact portable MicroNIR spectrometer were correlated with their crosslink density levels, which were referencely evaluated by the toluene swell index (TSI). The machine learning protocols used to classify the 3 groups of TSI were specified as less than 80% TSI, 80–88% TSI, and more than 88% TSI. The 80–88% TSI group was the group in which the compounded latex was suitable for medical glove production, which made the glove specification comply with the requirements of customers as indicated by the tensile test. The results show that when comparing the algorithms used for modeling, the linear discriminant analysis (LDA) developed by 2nd derivative spectra with 15 k-best selected wavelengths fairly accurately predicted the class but was most reliable among other algorithms, i.e., artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (kNN), due to higher prediction accuracy, precision, recall, and F1-score of the same value of 0.76 and no overfitting or underfitting prediction. This developed model can be implemented in the glove factory for screening purposes in the production line. However, deep learning modeling should be explored with a larger sample number required for better model performance.

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“…An algorithmic model that can judge the problems of the human brain from the perspective of information processing. The ability to approximate and the information is used to complete the simplest abstract calculations [23]. The neural network is composed of an input layer, an output layer, and one or more layers of hidden layers, which are given corresponding weights through the connection between neurons, and the weights are continuously adjusted through training and learning algorithms to finally reach the optimum.…”

Section: Modeling Methodsmentioning

confidence: 99%

Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index

Gao,

Zhang,

et al. 2023

Preprint

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Accurate estimation of chlorophyll content in wheat leaves can not only reflect its growth and nutritional status, but also provide a scientific basis for field management. In order to evaluate the potential of hyperspectral data to estimate the chlorophyll content of wheat leaves, this study focused on the leaves of wheat at the flag-picking stage, flowering stage, grain filling stage and maturity stage. Based on the framework of five vegetation indexes, the spectral index was con-structed by using the combination of 400-1000nm bands, and the correlation between the con-structed spectral index and the measured chlorophyll value was analyzed, and the optimal spectral index was screened by the correlation coefficient. Based on the optimal spectral index, polynomial regression, random forest, decision tree and artificial neural network were used to establish the estimation model of chlorophyll value, and the optimal model for estimating the chlorophyll value of wheat leaves was selected through model evaluation. The results showed that the five optimal spectral indices at the four growth stages were mainly composed of red band, red edge band and near-infrared band, and the five optimal spectral indices at the grain filling stage had the highest correlation with the chlorophyll value, and the absolute value of the cor-relation coefficient was greater than 0.73, the accuracy of the estimation model established in the four growth stages was different, and the estimation accuracy of the flag stage was the best, with R² and RMSE of 0.79 and 2.63, respectively. The above results show that hyperspectral data is suitable for estimating the chlorophyll value of wheat leaves, and the PR model of flag picking period can be used as the optimal model for estimating the chlorophyll value of wheat leaves.

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Prediction of winter wheat leaf chlorophyll content based on VIS/NIR spectroscopy using ANN and PLSR

Cited by 14 publications

References 60 publications

Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index

Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index

Classification of the Crosslink Density Level of Para Rubber Thick Film of Medical Glove by Using Near-Infrared Spectral Data

Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index

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