High performance dual field of view spectroradiometer with novel input optics for, autonomous reflectance measurements over an extended spectral range

MacLellan, Christopher; Malthus, Tim J.

doi:10.1109/igarss.2009.5417905

Cited by 2 publications

(2 citation statements)

References 6 publications

(8 reference statements)

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“…Hyperspectral measurements of plant canopy using the sun as the illumination source can be challenging [5], especially under changing atmospheric conditions, requiring frequent reference data collection by the same or another sensor. MacLellan and Malthus [6] presented the concept of a spectrometer with a dual field-of-view which alternately obtains upwelling (radiance) and downwelling (irradiance) measurements using a cosine response fore optic [7] in lieu of the standard white reference. MacArthur et al [8] designed and presented the Piccolo, an operational dual field-of-view system, and in the current study, it was mounted on a tractor in soybean experimental plots.…”

Section: Introductionmentioning

confidence: 99%

Spectral Data Collection by Dual Field-of-View System under Changing Atmospheric Conditions—A Case Study of Estimating Early Season Soybean Populations

Herrmann

Vosberg

Townsend

et al. 2019

Sensors

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There is an increasing interest in using hyperspectral data for phenotyping and crop management while overcoming the challenge of changing atmospheric conditions. The Piccolo dual field-of-view system collects up- and downwelling radiation nearly simultaneously with one spectrometer. Such systems offer great promise for crop monitoring under highly variable atmospheric conditions. Here, the system’s utility from a tractor-mounted boom was demonstrated for a case study of estimating soybean plant populations in early vegetative stages. The Piccolo system is described and its performance under changing sky conditions are assessed for two replicates of the same experiment. Plant population assessment was estimated by partial least squares regression (PLSR) resulting in stable estimations by models calibrated and validated under sunny and cloudy or cloudy and sunny conditions, respectively. We conclude that the Piccolo system is effective for data collection under variable atmospheric conditions, and we show its feasibility of operation for precision agriculture research and potential commercial applications.

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Section: Introductionmentioning

confidence: 99%

Spectral Data Collection by Dual Field-of-View System under Changing Atmospheric Conditions—A Case Study of Estimating Early Season Soybean Populations

Herrmann

Vosberg

Townsend

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Proximal measurements using the sun as illumination can be a challenge [36,38]. MacLellan and Malthus [39] presented the concept of a two spectrometers system with dual field of view for each, alternately obtaining upwelling (radiance) and downwelling (irradiance) measurements, using a cosine response fore optic [40] in lieu of the standard white reference. MacArthur et al [41] designed and presented an operational system (Piccolo) that is lightweight, self-contained, and wirelessly controlled, and similar in principle to dual field of view systems used at eddy covariance towers for canopy reflectance observations [42,43].…”

Section: Introductionmentioning

confidence: 99%

Leaf and Canopy Level Detection of Fusarium Virguliforme (Sudden Death Syndrome) in Soybean

et al. 2018

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Pre-visual detection of crop disease is critical for food security. Field-based spectroscopic remote sensing offers a method to enable timely detection, but still requires appropriate instrumentation and testing. Soybean plants were spectrally measured throughout a growing season to assess the capacity of leaf and canopy level spectral measurements to detect non-visual foliage symptoms induced by Fusarium virguliforme (Fv, which causes sudden death syndrome). Canopy reflectance measurements were made using the Piccolo Doppio dual field-of-view, two-spectrometer (400 to 1630 nm) system on a tractor. Leaf level measurements were obtained, in different plots, using a handheld spectrometer (400 to 2500 nm). Partial least squares discriminant analysis (PLSDA) was applied to the spectroscopic data to discriminate between Fv-inoculated and control plants. Canopy and leaf spectral data allowed identification of Fv infection, prior to visual symptoms, with classification accuracy of 88% and 91% for calibration, 79% and 87% for cross-validation, and 82% and 92% for validation, respectively. Differences in wavelengths important to prediction by canopy vs. leaf data confirm that there are different bases for accurate predictions among methods. Partial least square regression (PLSR) was used on a late-stage canopy level data to predict soybean seed yield, with calibration, cross-validation and validation R 2 values 0.71, 0.59 and 0.62 (p < 0.01), respectively, and validation root mean square error of 0.31 t•ha −1. Spectral data from the tractor mounted system are thus sensitive to the expression of Fv root infection at canopy scale prior to canopy symptoms, suggesting such systems may be effective for precision agricultural research and management.

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High performance dual field of view spectroradiometer with novel input optics for, autonomous reflectance measurements over an extended spectral range

Cited by 2 publications

References 6 publications

Spectral Data Collection by Dual Field-of-View System under Changing Atmospheric Conditions—A Case Study of Estimating Early Season Soybean Populations

Spectral Data Collection by Dual Field-of-View System under Changing Atmospheric Conditions—A Case Study of Estimating Early Season Soybean Populations

Leaf and Canopy Level Detection of Fusarium Virguliforme (Sudden Death Syndrome) in Soybean

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