Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice

Das, Bappa; Manohara, K. K.; Mahajan, Gopal Ramdas; Sahoo, Rabi Narayan

doi:10.1016/j.saa.2019.117983

Cited by 64 publications

(31 citation statements)

References 35 publications

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“…The variations in the spectral data were more prominent in the NIR region than in the visible. Similar findings were noted by [85] and [20] while predicting the leaf ion content using remote sensing in cotton and rice, respectively. The spectral pattern and variations recorded made the spectral data suitable for further analysis.…”

Section: Variations In Leaf Nutrient Concentrations and Spectral Datasupporting

confidence: 81%

“…Over the last two decades, advancements in remote sensing technologies such as the use of reflectance spectroscopy, airborne and satellite technology, and statistical analysis approaches thereof have made it easy to understand several key processes and components of plants such as plant population [1][2][3], grain yield and biomass [4][5][6][7][8], pigment or chlorophyll [9][10][11], water stress response [12][13][14][15], nutritional status [16][17][18][19][20][21] or pest and disease identification [22][23][24][25]. Yet, in-field proximal sensing to estimate the nutritional status of the crops is an economical and technical challenge [26].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning models are a unique and robust technique to analyze and model any data being non-linear and non-parametric [21,52,53]. Use of a combination of linear multivariate analysis models such as PLSR and principal component analysis (PCA) with non-linear and non-parametric models such as artificial neural network (ANN) [54][55][56][57] elastic net (ELNET), support vector regression (SVR), Gaussian process regression (GPR), multivariate adaptive regression spline (MARS), random forest (RF), extreme gradient boosting (XGB), generalized additive model (GAM), and k-nearest neighbor (KNN) [20] has been reported to retrieve information from spectral features. The machine learning models are capable of performing numerous calculations in several combinations and are thus useful to reduce the time involved in the analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Some of the commonly employed multivariate modeling techniques to extract information from hyperspectral data and to establish a relationship between spectral reflectance and measured variables are stepwise MLR [59,60], PLSR [61,62], successive projections algorithm coupled with MLR [63,64], ANN [63,65], and SVMR [47,66]. Very recently, a combination of the PCA and PLSR combined learning models have been used by [20] as a non-destructive tool, to predict the leaf ion (K, Na, Ca, and Mg) content for phenotyping of rice to salt-stress. They found the prediction accuracy of different approaches in order as PLSR-combined models > PCA-combined models > indices-based models.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

et al. 2021

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Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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Section: Variations In Leaf Nutrient Concentrations and Spectral Datasupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

et al. 2021

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“…Due to the above mentioned reasons, NIR spectroscopy has been widely used for nondestructive measurements in a variety of domains, such as food, [13,14] pharmacy, [15,16] petrochemical, [17,18] and chemical engineering. [19][20][21] Recently, chemometrics and machine learning methods further push forward the development of NIR spectroscopy, [22][23][24][25] which help to extract feature information from the spectra and serve as a basic tool for classification or regression. [26,27] A series of studies exist in the literatures that characterize the dispersion state using NIR spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

In‐line characterization of dispersion uniformity evolution during a twin‐screw blending extrusion based on near‐infrared spectroscopy

Zhang

Lei

et al. 2020

Polymer Engineering & Sci

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Properties of an immiscible polymer blend have been proved to be closely related to dispersion uniformity of the minor phase. At present, dispersion uniformity is difficult to evaluate during the blending process, resulting in hysteretic feedback. Aiming at this problem, this work utilized near‐infrared (NIR) spectroscopy to in‐line characterize dispersion uniformity evolution during a twin‐screw extrusion. A multichannel NIR measurement system was set up and applied to evaluate the blending process of polypropylene and polyolefin elastomer (POE). Based on the NIR spectra collected at different positions of the extruder, five prediction models of POE content were established using the light gradient boosting machine algorithm. Dispersion uniformity was characterized through the fluctuation of the predicted content. The evolution of dispersion under such processing parameters was consistent with scanning electron microscopy.

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Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning

Saini,

Rana,

Halder

et al. 2024

The Plant Phenome Journal

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Fusarium head blight (FHB) is one of the most destructive fungal diseases affecting wheat (Triticum aestivum). Moreover, it is notorious for producing mycotoxin deoxynivalenol (DON), posing a significant global threat to food and feed safety. Traditional methods like enzyme‐linked immunosorbent assay (ELISA) and gas chromatography‐mass spectrometry (GC‐MS) are commonly used to assess DON levels in grain or flour samples and are time‐consuming and expensive. Therefore, a faster, cost‐effective method to estimate DON content is needed, especially for enhancing breeding efforts to reduce DON levels in wheat. In this study, we envisioned integrating close‐range hyperspectral imaging with deep learning (DL) models to estimate DON content in wheat meal/flour. We selected 243 advanced breeding lines from the South Dakota State University (SDSU) wheat breeding program that were evaluated in FHB nurseries (2019–2020 and 2020–2021). The wheat meal samples were analyzed for DON content using GC‐MS and subsequently subjected to close‐range hyperspectral imaging. We evaluated three conventional machine learning (ML), two DL models and data augmentation. Among the conventional ML models, partial least squares regression (PLSR) (with R2P = 0.88 and 0.90 for original and augmented datasets, respectively) demonstrated the highest prediction accuracies for DON content. However, the one‐dimensional convolutional neural network (1D‐CNN) achieved the highest prediction accuracies (R2P = 0.90 and = 0.96 for original and augmented datasets, respectively) compared to all tested models and demonstrated the lowest error. In conclusion, integration of advanced hyperspectral imaging with ML approaches exhibits significant potential for high‐throughput and cost‐effective estimation of DON content in wheat, thereby accelerating wheat breeding efforts for reduced DON levels.

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Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice

Cited by 64 publications

References 35 publications

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

In‐line characterization of dispersion uniformity evolution during a twin‐screw blending extrusion based on near‐infrared spectroscopy

Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning

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