Systems and Methods for Automated Hyperspectral Vegetation Index Derivation for High-Throughput Plant Phenotyping

Patent metadata

Field	Value
Publication number	US-2024096092-A1
Application number	US-202218272728-A
Country	US
Kind code	A1
Filing date	Jan 27, 2022
Priority date	Jan 29, 2021
Publication date	Mar 21, 2024
Grant date	—

Abstract

The present invention is directed to a method for automated hyperspectral vegetation index (VI) determination, including: accessing measured spectra and respective measured ground truth values of a selected vegetation trait; accessing a library of VI models, each model including a relationship defining an index value for the vegetation trait by mathematically combining spectral measurement values at a plurality of wavebands; selecting a VI model from the library; generating a hyperparameter for each of the spectral measurement values of the selected model, the hyperparameter including a selected waveband for each of the plurality of model wavebands; evaluating the selected model with the selected wavebands with an objective function score; a model parameter tuning step using an optimizer to select the waveband for each of the at least two wavebands based on sequential model-based optimization (SMBO); and repeating the model selection, generation, evaluation and tuning steps for a plurality of iterations.

First claim

1 . A method for automated hyperspectral vegetation index (VI) determination, the method including:
accessing measured spectra and respective measured ground truth values of a selected vegetation trait; accessing a library of VI models, wherein each VI model includes a relationship defining an index value for the vegetation trait by mathematically combining spectral measurement values at a plurality of wavebands (“model wavebands”), optionally with one or more coefficients (“model coefficients”); a model selection step, including selecting a VI model from the library of VI models; a model parameter generation step, including:
generating a hyperparameter for each of the spectral measurement values of the selected VI model, wherein the hyperparameter includes a selected waveband for each of the plurality of model wavebands, and
generating a hyperparameter for each of the model coefficients of the selected VI model if the selected VI model has any coefficients, wherein the hyperparameter includes a selected coefficient value for each of the model coefficients;
a model evaluation step, including evaluating the selected VI model with the selected wavebands and optionally selected coefficient values with an objective function score, wherein the objective function score quantifies a closeness of fit between the ground truth values and calculated VI values from the selected VI model with the generated hyperparameters and the respective measured spectra; a model parameter tuning step, including using an optimizer to select the waveband for each of the at least two wavebands (“optimum wavebands”), and optionally to select the coefficient values for each of the coefficients (“optimum coefficient values”) based on sequential model-based optimization (SMBO); and repeating the model selection step, the model parameter generation step, the model evaluation step and the model parameter tuning step (together referred to as the “optimization steps”) for a plurality of iterations.

2 . The method of claim 1 , including:
selecting the VI model from the plurality of iterations with the selected optimum wavebands and optimum coefficient values, which is the VI model with model parameters that generates the highest objective function score over all iterations.

3 . The method of claim 1 , including:
a grouping step, including grouping VI models from the library according the number (Nwb) of the model wavebands, including a first group with a plurality of two-waveband models (Nwb=2) and a second group with a plurality of three-waveband models (Nwb=3); a running step, including determining the best-performing VI model within each group by performing the plurality of the iterations of the optimization steps for each group; and a cross-group comparison step, including selecting an overall best VI model from the best-performing VI models based on their respective objective function scores.

4 . The method of claim 1 , including:
creating the library of VI models.

5 . The method of claim 1 , wherein the SMBO is Bayesian SMBO and the optimizer is a Bayesian optimizer.

6 . The method of claim 5 , wherein the Bayesian optimizer is a Tree-Structured Parzen Estimator (TPE).

7 . The method of claim 1 , including:
analysing samples of the plant to generate the measured spectra and the ground truth values of the plant.

8 . The method of claim 7 , wherein the measured spectra include reflectance spectra.

9 . The method of claim 7 , including:
using a hyperspectral imaging sensor or spectrometer to generate the measured spectra.

10 . The method of claim 7 , wherein the analysing of the samples of the plant includes: imaging the plants at a plurality of mutually different angles.

11 . The method of claim 10 , wherein the plurality of mutually different angles includes 0°, 120°, and 240°.

12 . The method of claim 10 , including: rotating the plants to the plurality of mutually different angles using a lifter and turner assembly.

13 . The method of claim 1 , wherein the model wavebands include a plurality of wavebands in one or more of:
a visible region with wavelengths 400-700 nm; a near infrared region with wavelengths 700-1000 nm; a shortwave infrared region with wavelengths 1000-2500 nm; a shortwave infrared region with wavelengths 1200-1700 nm; a region with wavelengths 1410-1430 nm; a region with wavelengths 1550-1680 nm; a near infrared region with wavelengths 800-900 nm; and a region with wavelengths 400-5,400 nm.

14 . The method of claim 1 , wherein the model wavebands include: over 1,000 wavebands, over 2,000 wavebands, over 3,000 wavebands, over 4,000 wavebands, or over 5,000 wavebands.

15 . The method of claim 14 , wherein a number of the wavebands is selected based on a number of the wavebands measured by a hyperspectral imaging sensor or spectrometer.

16 . A system configured to perform the method of claim 1 , the system including:
an optimizer module configured to perform the optimization steps, including the model selection step, the model parameter generation step, the model parameter tuning step and the model evaluation step; and optionally one or more hyperspectral sensors.

17 . (canceled)

18 . The system of claim 16 , including: an unmanned aerial vehicle (UAV) system with the one or more hyperspectral sensors.

19 . The system of claim 16 , including:
a hyperspectral imaging station to generate the spectrum; and a lifter and turner assembly for imaging plants at a plurality of mutually different angles to generate the measured spectra of the plant.

20 . The system of claim 19 , wherein the hyperspectral imaging station includes a pushbroom-type imaging spectrometer, optionally operational over a spectral range of 475-1710 nm and a spectral resolution of less than 10 nm.

21 . Machine-readable storage media including machine readable instructions that, when executed by a computing system, perform data-processing steps of the method of claim 1 , including one or more of the accessing steps, the model selection step, the model parameter generation step, the model parameter tuning step, the model selection step, the grouping step, the running step, and the cross-group comparison step.

Assignees

• Agriculture Victoria Serv Pty

Inventors

• Koh Joshua Chee Oon
• Banerjee Bikram Pratap
• Spangenberg German Carlos
• Kant Surya

Classifications

• G06V10/87

  using selection of the recognition techniques, e.g. of a classifier in a multiple classifier system · CPC title

• G06V10/16

  using multiple overlapping images; Image stitching · CPC title

• G06V20/17

  taken from planes or by drones · CPC title

• G06V10/774

  Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06V10/143

  Sensing or illuminating at different wavelengths · CPC title