Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods

Yu, Ke; Fang, Shiyan; Zhao, Yanru

doi:10.1016/j.saa.2020.118917

Cited by 44 publications

(17 citation statements)

References 42 publications

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“…Support vector machines (SVM), K-nearest neighbours and partial least squares (PLS) regression models were developed and evaluated in terms of the ability to diagnose the disease; the results showed that the SVM model obtained an accuracy of 97.5%. More recently, Yu et al 16 investigated the detection of mercury stress in tobacco plants using hyperspectral imaging and machine learning. Features describing the appearance and texture of tobacco plants were examined.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Metaheuristic algorithms in visible and near infrared spectra to detect excess nitrogen content in tomato plants

Pourdarbani

Sabzi

Rohban

et al. 2022

Journal of Near Infrared Spectroscopy

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Chemical fertilizers are widely applied in agriculture to achieve high yield, enhance produce quality and build resistance to diseases; in our case the plant being tomato ( Solanum lycopersicum L. var. Royal). However, the acidity, size and taste of tomato fruits could change with excess nitrogen (N) application. The present study aims at the early detection of nitrogen-rich tomato leaves using hyperspectral imaging techniques in the visible and near infrared (Vis-NIR) spectrum, in order to improve plant nutrition composition at an early growth stage. A 30% over-dose of nitrogen was applied to half of the tomato pots. Five leaves were randomly collected from each pot for 3 days (classes D0, D1, D2 and D3), and images were captured with a hyperspectral camera. A metaheuristic approach of artificial neural networks and the firefly algorithm (ANN-FA) was used to determine the most discriminative wavelengths. Afterwards, a combination of ANN and particle swarm optimization (ANN-PSO) was used to classify tomato leaves into the four classes. The training/classification process was repeated 200 times, and results indicated that the proposed approach was able to detect the excess of nitrogen even at the first day (D1), with a precision of 92.9%. Considering all the classes, the average correct classification rate was 92.6%, while the best execution achieved 95.5% accuracy. Thus, the method showed a high performance for practical uses.

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Section: Introduction and Literature Reviewmentioning

confidence: 99%

Metaheuristic algorithms in visible and near infrared spectra to detect excess nitrogen content in tomato plants

Pourdarbani

Sabzi

Rohban

et al. 2022

Journal of Near Infrared Spectroscopy

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“…In detail, the spectral reflectance in the visible light region of 400–600 nm is relatively low, which may be caused by the strong absorption of light by the pigments in the dried Hami jujube. Approximately in the 600–750 nm spectral range, the reflectance rises sharply, which could be ascribed to the ‘red edge’ of individual organisms [ 33 ]. The weak valley near 890 nm corresponded to the third overtone of C-H [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

Detecting Starch-Head and Mildewed Fruit in Dried Hami Jujubes Using Visible/Near-Infrared Spectroscopy Combined with MRSA-SVM and Oversampling

et al. 2022

Foods

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Dried Hami jujube has great commercial and nutritional value. Starch-head and mildewed fruit are defective jujubes that pose a threat to consumer health. A novel method for detecting starch-head and mildewed fruit in dried Hami jujubes with visible/near-infrared spectroscopy was proposed. For this, the diffuse reflectance spectra in the range of 400–1100 nm of dried Hami jujubes were obtained. Borderline synthetic minority oversampling technology (BL-SMOTE) was applied to solve the problem of imbalanced sample distribution, and its effectiveness was demonstrated compared to other methods. Then, the feature variables selected by competitive adaptive reweighted sampling (CARS) were used as the input to establish the support vector machine (SVM) classification model. The parameters of SVM were optimized by the modified reptile search algorithm (MRSA). In MRSA, Tent chaotic mapping and the Gaussian random walk strategy were used to improve the optimization ability of the original reptile search algorithm (RSA). The final results showed that the MRSA-SVM method combined with BL-SMOTE had the best classification performance, and the detection accuracy reached 97.22%. In addition, the recall, precision, F1 and kappa coefficient outperform other models. Furthermore, this study provided a valuable reference for the detection of defective fruit in other fruits.

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“…All modeling analyses were conducted in Rstudio (PBC, v4.0.4) (Team, 2020). The pipeline has two independent phases: (1) transformations and outlier detection and (2) model training and model selection (Yu et al, 2021). To correct the effects of light scattering or highlight the differences in absorption of light at different wavelengths, different spectral pretreatments, including standard normal variate (SNV), the first-order and the secondorder differential with Savitzky-Golay smoothing along with their combinations were systematically applied to the averaged spectrum per sample (Alchanatis et al, 2005;Li et al, 2019).…”

Section: Near Infrared Reflectance Spectroscopy Data Analysismentioning

confidence: 99%

Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen Content Under Drought Stress Using Near Infrared Spectroscopy

Liu

Tomasetto

et al. 2022

Front. Plant Sci.

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Drought is a climatic event that considerably impacts plant growth, reproduction and productivity. Toona sinensis is a tree species with high economic, edible and medicinal value, and has drought resistance. Thus, the objective of this study was to dynamically monitor the physiological indicators of T. sinensis in real time to ensure the selection of drought-resistant varieties of T. sinensis. In this study, we used near-infrared spectroscopy as a high-throughput method along with five preprocessing methods combined with four variable selection approaches to establish a cross-validated partial least squares regression model to establish the relationship between the near infrared reflectance spectroscopy (NIRS) spectrum and physiological characteristics (i.e., chlorophyll content and nitrogen content) of T. sinensis leaves. We also tested optimal model prediction for the dynamic changes in T. sinensis chlorophyll and nitrogen content under five separate watering regimes to mimic non-destructive and dynamic detection of plant leaf physiological changes. Among them, the accuracy of the chlorophyll content prediction model was as high as 72%, with root mean square error (RMSE) of 0.25, and the RPD index above 2.26. Ideal nitrogen content prediction model should have R2 of 0.63, with RMSE of 0.87, and the RPD index of 1.12. The results showed that the PLSR model has a good prediction effect. Overall, under diverse drought stress treatments, the chlorophyll content of T. sinensis leaves showed a decreasing trend over time. Furthermore, the chlorophyll content was the most stable under the 75% field capacity treatment. However, the nitrogen content of the plant leaves was found to have a different and variable trend, with the greatest drop in content under the 10% field capacity treatment. This study showed that NIRS has great potential for analyzing chlorophyll nitrogen and other elements in plant leaf tissues in non-destructive dynamic monitoring.

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Heavy metal Hg stress detection in tobacco plant using hyperspectral sensing and data-driven machine learning methods

Cited by 44 publications

References 42 publications

Metaheuristic algorithms in visible and near infrared spectra to detect excess nitrogen content in tomato plants

Metaheuristic algorithms in visible and near infrared spectra to detect excess nitrogen content in tomato plants

Detecting Starch-Head and Mildewed Fruit in Dried Hami Jujubes Using Visible/Near-Infrared Spectroscopy Combined with MRSA-SVM and Oversampling

Non-destructive Measurements of Toona sinensis Chlorophyll and Nitrogen Content Under Drought Stress Using Near Infrared Spectroscopy

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