Inverse Modeling for Characteristic Prediction from Multi-Spectral and Hyper-Spectral Remote Sensed Datasets
First Claim
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1. A method of estimating a plant characteristic, comprising:
- a. building a predictive model using inverse modeling using;
i. a first set of spectroscopic data from a first plant population, andii. corresponding measured characteristic data sets from the first plant population; and
,b. applying the model to a second set of spectroscopic data from a second plant, a second plant population, or both, so as to estimate the characteristic in the second plant.
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Abstract
Provided are methods and related devices for predicting the presence or level of one or more characteristics of a plant or plant population based on spectral, multi-spectral, or hyper-spectral data obtained by, e.g., remote sensing. The predictions and estimates furnished by the inventive methods and devices are useful in crop management, crop strategy, and optimization of agricultural production.
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Citations
32 Claims
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1. A method of estimating a plant characteristic, comprising:
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a. building a predictive model using inverse modeling using; i. a first set of spectroscopic data from a first plant population, and ii. corresponding measured characteristic data sets from the first plant population; and
,b. applying the model to a second set of spectroscopic data from a second plant, a second plant population, or both, so as to estimate the characteristic in the second plant. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18)
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19. A method of predicting drought tolerance of a plant, comprising:
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a. building a predictive model by inverse modeling of spectroscopic data collected from a first population of plants and corresponding measured drought tolerance data from the first population of plants; and b. applying the predictive model to spectroscopic data collected from a second plant, a second plant population, or both to estimate the drought tolerance of the second plant, plant population, or both.
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20. A method of predicting the level of a target analyte in a plant, comprising:
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a. providing a set of spectral data from one or more plants corresponding to one or more reference value concentrations of an analyte of interest in the one or more plants; b. constructing a predictive model between the calibration spectra and the reference value concentrations wherein the predictive model is constructed using inverse modeling based on an optimal number of factors to model at least a portion of said sample spectrum; and c. generating a vector of calibration coefficients where said vector constitutes said predictive model and wherein a specific number of factors models at least one region of a spectrum.
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21. A method of estimating a plant characteristic, comprising:
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a. using at least one computer processor to construct by inverse modeling a predictive model from (i) a first set of spectroscopic data from a first plant population and (ii) corresponding measured data for the characteristic in at least a portion of the first population; and b. applying the predictive model to a second set of spectroscopic data from a second plant, a second plant population, or both, to estimate the characteristic'"'"'s presence in the second plant, the second plant population, or both.
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22. A system for estimating a plant characteristic, comprising:
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a. a device capable of collecting spectroscopic absorbance data from one or more plants physically distant from the device; b. a memory unit capable of storing collected spectroscopic absorbance data, measured values of a plant characteristic corresponding to the spectroscopic absorbance data, or both; and c. a computing device capable of correlating, by inverse modeling, at least a portion of the spectroscopic absorbance data to one or more measured values of a plant characteristic corresponding to the spectroscopic absorbance data. - View Dependent Claims (23)
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24. A method of predicting a level of genome introgression for a backcross experiment comprising:
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a. building a predictive model by inverse modeling principles based on chemometric analysis of spectroscopic data from at least a first plant and corresponding measured level of genome introgression data as input variables; and b. applying the model to a spectroscopic data set from at least a second plant, a second plant population, or both, to estimate the level of genome introgression in the second plant, the second plant population, or both. - View Dependent Claims (25, 26, 27, 28, 29, 30, 31, 32)
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Specification