Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

Liang, Jing; Liu, Yuanyuan; Zhu, Panpan; He, Yong

doi:10.3390/app9102092

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…The development of ML pipelines is the next generation solution to optimize the data processing stage that lends itself to the early detection of plant disease [22]. Indeed, Liang et al [23] set up a feasible method for the detection of Sclerotinia stem rot in Arabidopsis thaliana (L.) Heynh. based on hyperspectral imaging coupled with extreme ML management to select three optimal wavelengths to achieve the overall accuracy of 93.7% in diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning

et al. 2021

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Fusarium oxysporum f. sp. raphani is responsible for wilting wild rocket (Diplotaxis tenuifolia L. [D.C.]). A machine learning model based on hyperspectral data was constructed to monitor disease progression. Thus, pathogenesis after artificial inoculation was monitored over a 15-day period by symptom assessment, qPCR pathogen quantification, and hyperspectral imaging. The host colonization by a pathogen evolved accordingly with symptoms as confirmed by qPCR. Spectral data showed differences as early as 5-day post infection and 12 hypespectral vegetation indices were selected to follow disease development. The hyperspectral dataset was used to feed the XGBoost machine learning algorithm with the aim of developing a model that discriminates between healthy and infected plants during the time. The multiple cross-prediction strategy of the pixel-level models was able to detect hyperspectral disease profiles with an average accuracy of 0.8. For healthy pixel detection, the mean Precision value was 0.78, the Recall was 0.88, and the F1 Score was 0.82. For infected pixel detection, the average evaluation metrics were Precision: 0.73, Recall: 0.57, and F1 Score: 0.63. Machine learning paves the way for automatic early detection of infected plants, even a few days after infection.

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Section: Introductionmentioning

confidence: 99%

Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning

et al. 2021

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“…In addition, distribution maps obtained using hyperspectral imaging techniques are now widely used in agricultural studies, forestry, meat quality testing, etc. [26][27][28][29]. Meanwhile, the use of hyperspectral imaging techniques for generating soil TAs concentration distribution maps using machine learning models remains to be studied [30].…”

Section: Introductionmentioning

confidence: 99%

Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil

Wei

Zhang

et al. 2020

Applied Sciences

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Soil total arsenic (TAs) contamination caused by human activities—such as mining, smelting, and agriculture—is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate total arsenic (TAs) concentration in soil. VNIR with hyperspectral imaging has the potential to predict TAs concentration in soil. In this study, 59 soil samples were collected from the Daye City mining area of China, and hyperspectral imaging of the soil samples was undertaken using a visible/near-infrared hyperspectral imaging system (wavelength range 470–900 nm). Spectral preprocessing included standard normal variate (SNV) transformation, multivariate scatter correction (MSC), first derivative (FD) preprocessing, and second derivative (SD) preprocessing. Characteristic bands were then identified based on Spearman’s rank correlation coefficients. Four regression models were used for the modeling prediction: partial least squares regression (PLSR) (R2 = 0.71, RMSE = 0.48), support vector machine regression (SVMR) (R2 = 0.78, RMSE = 0.42), random forest (RF) (R2 = 0.78, RMSE = 0.42), and extremely randomized trees regression (ETR) (R2 = 0.81, RMSE = 0.38). The prediction results were compared with the results of atomic fluorescence spectrometry methods. In the prediction results of the models, the accuracy of ETR using FD preprocessing was the highest. The results confirmed that hyperspectral imaging combined with Spearman’s rank correlation with machine learning models can be used to estimate soil TAs content.

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Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review

Lassalle

2021

Science of The Total Environment

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Hyperspectral Reflectance Imaging Combined with Multivariate Analysis for Diagnosis of Sclerotinia Stem Rot on Arabidopsis Thaliana Leaves

Cited by 5 publications

References 32 publications

Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning

Early Detection of Wild Rocket Tracheofusariosis Using Hyperspectral Image-Based Machine Learning

Development of Visible/Near-Infrared Hyperspectral Imaging for the Prediction of Total Arsenic Concentration in Soil

Monitoring natural and anthropogenic plant stressors by hyperspectral remote sensing: Recommendations and guidelines based on a meta-review

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