Detection of fungal infection and Ochratoxin A contamination in stored barley using near-infrared hyperspectral imaging

Senthilkumar, T.; Jayas, Digvir S.; White, N. D. G.; Fields, Paul G.; Gräfenhan, Tom

doi:10.1016/j.biosystemseng.2016.03.010

Cited by 46 publications

(21 citation statements)

References 19 publications

(22 reference statements)

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“…Existing studies utilizing hyperspectral imaging sensors are sparse compared with those utilizing non-imaging hyperspectral sensors. Field ASD hyperspectral radiometers have long been used in agricultural applications [16], such as the inversion of crop parameters [17][18][19], crop disease and pest monitoring [20][21][22], etc. Pushbroom hyperspectral sensors have been developed and deployed for agricultural applications.…”

Section: Introductionmentioning

confidence: 99%

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Yang

Wang

et al. 2017

Remote Sensing

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Abstract:Hyperspectral remote sensing is used in precision agriculture to remotely and quickly acquire crop phenotype information. This paper describes the generation of a digital orthophoto map (DOM) and radiometric calibration for images taken by a miniaturized snapshot hyperspectral camera mounted on a lightweight unmanned aerial vehicle (UAV). The snapshot camera is a relatively new type of hyperspectral sensor that can acquire an image cube with one spectral and two spatial dimensions at one exposure. The images acquired by the hyperspectral snapshot camera need to be mosaicked together to produce a DOM and radiometrically calibrated before analysis. However, the spatial resolution of hyperspectral cubes is too low to mosaic the images together. Furthermore, there are no systematic radiometric calibration methods or procedures for snapshot hyperspectral images acquired from low-altitude carrier platforms. In this study, we obtained hyperspectral imagery using a snapshot hyperspectral sensor mounted on a UAV. We quantitatively evaluated the radiometric response linearity (RRL) and radiometric response variation (RRV) and proposed a method to correct the RRV effect. We then introduced a method to interpolate position and orientation system (POS) information and generate a DOM with low spatial resolution and a digital elevation model (DEM) using a 3D mesh model built from panchromatic images with high spatial resolution. The relative horizontal geometric precision of the DOM was validated by comparison with a DOM generated from a digital RGB camera. A surface crop model (CSM) was produced from the DEM, and crop height for 48 sampling plots was extracted and compared with the corresponding field-measured crop height to verify the relative precision of the DEM. Finally, we applied two absolute radiometric calibration methods to the generated DOM and verified their accuracy via comparison with spectra measured with an ASD Field Spec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA). The DOM had high relative horizontal accuracy, and compared with the digital camera-derived DOM, spatial differences were below 0.05 m (RMSE = 0.035). The determination coefficient for a regression between DEM-derived and field-measured crop height was 0.680. The radiometric precision was 5% for bands between 500 and 945 nm, and the reflectance curve in the infrared spectral region did not decrease as in previous research. The pixel and data sizes for the DOM corresponding to a field area of approximately 85 m × 34 m were small (0.67 m and approximately 13.1 megabytes, respectively), which is convenient for data transmission, preprocessing and analysis. The proposed Remote Sens. 2017, 9, 642; doi:10.3390/rs9070642 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 642 2 of 21 method for radiometric calibration and DOM generation from hyperspectral cubes can be used to yield hyperspectral imagery products for various applications, particularly precision agriculture.

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Section: Introductionmentioning

confidence: 99%

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Yang

Wang

et al. 2017

Remote Sensing

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“…[92] Contamination by microbial toxins in plant-based foods Microbial toxins are among the most controlled food contaminants and its consumption can lead to health problems such as jaundice, liver carcinomas, esophageal cancer, neural tube defects, immunosuppression, etc. [95,96] These substances are metabolites produced by fungi (mycotoxins) and bacteria (bacterial toxins), and the most common in food products are aflatoxins produced by A. flavus and A. parasiticus, [97] ochratoxin produced by Aspergillus, [96,98] fumonisins and trichothecenes produced by Fusarium, [96] and patulin produced by Penicillium, Aspergillus, or Byssochlamys. [99] Analytical techniques such as ELISA, HPLC, TLC, GC, and visual analytical techniques such as fluorescence-based detection methods [43,45,[48][49][50] are applied for detecting toxins in food products.…”

Section: Microbial Contamination In Agricultural Cropsmentioning

confidence: 99%

“…For its part, the presence of ochratoxin A in stored barley has been recently evaluated by Senthilkumar et al [98] using NIR-HSI (reflectance mode at 1000-1600 nm). They have applied PCA to select key wavelengths: 1260, 1310, and 1360 nm (to evaluate Aspergillus glaucus, Penicillium spp., and non-ochratoxin A producing Penicillium verrucosum-infected grains), and 1310, 1360, and 1480 nm (to differentiate ochratoxin A contaminated grains).…”

Section: Microbial Contamination In Agricultural Cropsmentioning

confidence: 99%

Evaluation of biological contaminants in foods by hyperspectral imaging: A review

Mantilla

Jara

Yimer

2017

International Journal of Food Properties

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Responding to the ever-growing concern about safe foods and security, the food industries are forced to seek an emerging technology capable of detecting and quantifying contaminations, especially those of biological origin. Among the different emerging technologies, hyperspectral imaging is considered a good alternative as it can be easily applied at all steps of the food production process and is a non-destructive technique. This paper reviews targeted analytical applications of hyperspectral imaging in monitoring biological contaminants in food. First, traditional techniques for detection of biological contaminants in foods are presented, where disadvantages for practical applications are highlighted and explained in detail. Second, prominent applications of hyperspectral imaging from the last decade to food safety and quality assessment are reviewed, specifically focusing on both deteriorative and pathogenic microorganisms, microbial toxins, and parasites; whether acting individually or collectively in spoiling food products and/or represent a health risk to the consumers. Finally, relevant current and future challenges, advantages and disadvantages of hyperspectral imaging applications are briefly examined.

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“…Very recent work by Senthilkumar et al (2017) documents the use of HSI to classify ochratoxin A (produced by Penicillium spp.) in stored wheat into five concentration levels, as well as a related study of same toxin in stored barley (Senthilkumar et al, 2016). A common feature of these HSI studies has been the limited number of kernels captured in a sample's image, often being fewer than 50, and often with kernels in precise alignment longitudinally and axially, which makes it difficult to estimate the level of damage of the lot from which the test sample was drawn.…”

Section: Introductionmentioning

confidence: 99%

Estimating percentages of fusarium-damaged kernels in hard wheat by near-infrared hyperspectral imaging

Delwiche

Rodríguez

Rausch

et al. 2019

Journal of Cereal Science

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Fusarium head blight (FHB) is among the most common fungal diseases affecting wheat, resulting in decreased yield, low-density kernels, and production of the mycotoxin deoxynivalenol, a compound toxic to humans and livestock. Human visual analysis of representative wheat samples has been the traditional method for FHB assessment in both official inspection and plant breeding operations. While not requiring specialized equipment, visual analysis is dependent on a trained and consistent workforce, such that in the absence of these aspects, biases may arise among inspectors and evaluation dates. This research was intended to avoid such pitfalls by using longer wavelength radiation than the visible using hyperspectral imaging (HSI) on individual kernels. Linear discriminant analysis models to differentiate between sound and scab-damaged kernels were developed based on mean of reflectance values of the interior pixels of each kernel at four wavelengths (1100, 1197, 1308, and 1394 nm). Other input variables were examined, including kernel morphological properties and histogram features from the pixel responses of selected wavelengths of each kernel. The results indicate the strong potential of HSI in estimating fusarium damage. However, improvement in aligning this procedure to visual analysis is hampered by the inherent level of subjectivity in visual analysis.

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Detection of fungal infection and Ochratoxin A contamination in stored barley using near-infrared hyperspectral imaging

Cited by 46 publications

References 19 publications

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

The DOM Generation and Precise Radiometric Calibration of a UAV-Mounted Miniature Snapshot Hyperspectral Imager

Evaluation of biological contaminants in foods by hyperspectral imaging: A review

Estimating percentages of fusarium-damaged kernels in hard wheat by near-infrared hyperspectral imaging

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