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Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology.

ABSTRACT: In our study, the feasibility of using visible/near infrared hyperspectral imaging technology to detect the changes of the internal components of Chlorella pyrenoidosa so as to determine the varieties of pesticides (such as butachlor, atrazine and glyphosate) at three concentrations (0.6 mg/L, 3 mg/L, 15 mg/L) was investigated. Three models (partial least squares discriminant analysis combined with full wavelengths, FW-PLSDA; partial least squares discriminant analysis combined with competitive adaptive reweighted sampling algorithm, CARS-PLSDA; linear discrimination analysis combined with regression coefficients, RC-LDA) were built by the hyperspectral data of Chlorella pyrenoidosa to find which model can produce the most optimal result. The RC-LDA model, which achieved an average correct classification rate of 97.0% was more superior than FW-PLSDA (72.2%) and CARS-PLSDA (84.0%), and it proved that visible/near infrared hyperspectral imaging could be a rapid and reliable technique to identify pesticide varieties. It also proved that microalgae can be a very promising medium to indicate characteristics of pesticides.

SUBMITTER: Shao Y

PROVIDER: S-EPMC4829843 | biostudies-other | 2016 Apr

REPOSITORIES: biostudies-other

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Project description:Background:To investigate potential effects of herbicide phytotoxic on crops, a major challenge is a lack of non-destructive and rapid methods to detect plant growth that could allow characterization of herbicide-resistant plants. In such a case, hyperspectral imaging can quickly obtain the spectrum for each pixel in the image and monitor status of plants harmlessly. Method:Hyperspectral imaging covering the spectral range of 380-1030 nm was investigated to determine the herbicide toxicity in rice cultivars. Two rice cultivars, Xiushui 134 and Zhejing 88, were respectively treated with quinclorac alone and plus salicylic acid (SA) pre-treatment. After ten days of treatments, we collected hyperspectral images and physiological parameters to analyze the differences. The score images obtained were used to explore the differences among samples under diverse treatments by conducting principal component analysis on hyperspectral images. To get useful information from original data, feature extraction was also conducted by principal component analysis. In order to classify samples under diverse treatments, full-spectra-based support vector classification (SVC) models and extracted-feature-based SVC models were established. The prediction maps of samples under different treatments were constructed by applying the SVC models using extracted features on hyperspectral images, which provided direct visual information of rice growth status under herbicide stress. The physiological analysis with the changes of stress-responsive enzymes confirmed the differences of samples under different treatments. Results:The physiological analysis showed that SA alleviated the quinclorac toxicity by stimulating enzymatic activity and reducing the levels of reactive oxygen species. The score images indicated there were spectral differences among the samples under different treatments. Full-spectra-based SVC models and extracted-feature-based SVC models obtained good results for the aboveground parts, with classification accuracy over 80% in training, validation and prediction set. The SVC models for Zhejing 88 presented better results than those for Xiushui 134, revealing the different herbicide tolerance between rice cultivars. Conclusion:We develop a reliable and effective model using hyperspectral imaging technique which enables the evaluation and visualization of herbicide toxicity for rice. The reflectance spectra variations of rice could reveal the stress status of herbicide toxicity in rice along with the physiological parameters. The visualization of the herbicide toxicity in rice would help to provide the intuitive vision of herbicide toxicity in rice. A monitoring system for detecting herbicide toxicity and its alleviation by SA will benefit from the remarkable success of SVC models and distribution maps.

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Identification of pesticide varieties by testing microalgae using Visible/Near Infrared Hyperspectral Imaging technology.

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