SYSTEMS AND METHODS FOR MONITORING AGRICULTURAL PRODUCTS

• US 20200132651A1
• Filed: 12/09/2019
• Published: 04/30/2020
• Est. Priority Date: 06/01/2012
• Status: Active Grant

• Current Assignee: AGERpoint, Inc.
• Original Assignee: AGERpoint, Inc.
• Inventors: McPeek, K.Thomas
• Granted Patent: US 11,181,517 B2
• Time in Patent Office: Days
• CPC Class Codes: G01B 11/24 for measuring contours or curvatures
• CPC Class Codes: G01B 5/0035 Measuring of dimensions of trees
• CPC Class Codes: G01N 2021/1797 in landscape, e.g. crops
• CPC Class Codes: G01N 2021/8466 Investigation of vegetal material, e.g. leaves, plants, fruits
• CPC Class Codes: G01N 21/251 Colorimeters; Construction thereof
• CPC Class Codes: G01N 21/31 Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry G01N21/72 takes precedence
• CPC Class Codes: G01N 21/359 using near infrared light
• CPC Class Codes: G01N 2201/0216 Vehicle borne
• CPC Class Codes: G01N 33/0098 Plants or trees wood G01N33/46
• CPC Class Codes: G01N 33/025 Fruits or vegetables

Abstract

The present invention relates to systems and methods for monitoring agricultural products. In particular, the present invention relates to monitoring fruit production, plant growth, and plant vitality. According to embodiments of the invention, a plant analysis system is configured determine a spectral signature of a plant based on spectral data, and plant color based on photographic data. The spectral signatures and plant color are associated with assembled point cloud data. Morphological data of the plant can be generated based on the assembled point cloud data. A record of the plant can be created that associates the plant with the spectral signature, plant color, spectral data, assembled point cloud data, and morphological data, and stored in a library.

Claims

1. A plant analysis system, comprising:

a) a data acquisition component configured to;

measure properties of a plant utilizing waveform light detection and ranging (LiDAR) to assemble point cloud data, wherein said point cloud data comprises a plurality of three dimensional vertices each of which represents an external surface associated with said plant;

collect photographic data of said plant;

collect spectral data for said plant; and

geo-register the point cloud data and photographic data;

wherein the plurality of said three dimensional vertices of said point cloud data is associated with global positioning satellite (GPS) data, said photographic data, and said spectral data;

b) a transport vehicle on which said data acquisition component is mounted and configured to assemble and geo-register said point cloud data, and collect said photographic data and said spectral data on said plant; and

c) a processor configured to;

determine a spectral signature of said plant based on said spectral data;

determine plant color based on said photographic data and associate said point cloud data with said plant color; and

generate morphological data of said plant based on said point cloud data, said morphological data comprising one or more of plant stem diameter, plant height, plant volume, and plant leaf density.

2. The plant analysis system of claim 1, wherein:

the processor generates said morphological data by segmenting said point cloud data to identify boundaries of said plant;

or

the processor classifies said morphological data to identify a plant feature, said plant feature comprising a branching structure, trunk, biomass, canopy, fruit, blossom, fruit cluster, or blossom cluster.

3. The plant analysis system of claim 1, wherein the processor utilizes said point cloud data and said plant color to discriminate a fruit, blossom, fruit cluster, or blossom cluster from a shadow based, at least in part, on analyzing a variation of said plant color and a geometric shape defined by the three dimensional vertices of said point cloud data.

4. The plant analysis system of claim 1, wherein said data acquisition component is further configured to measure atmospheric conditions, and wherein said processor determines a phenotype of the plant based on said spectral signature, said morphological data, and said atmospheric conditions.

5. The plant analysis system of claim 1, wherein said processor is further configured to determine a vegetation index of one or more plants based on said spectral data.

6. The plant analysis system of claim 1, wherein said processor is further configured to determine a number and a size of fruits or blossoms on the plant, wherein the number and size of fruits or blossoms on the plant is based one or more of said point cloud data or said plant color data.

7. The plant analysis system of claim 6, wherein:

said number and size of fruits or blossoms on the plant is determined by clustering said point cloud data and plant color data; and

a crop yield is estimated based, at least in part, on said number and size of fruits or blossoms on the plant.

8. The plant analysis system of claim 1, wherein said processor is further configured to compare the spectral signature of the plant with a second spectral signature from a library of plant records.

9. The plant analysis system of claim 8, wherein said processor is further configured to:

detect a presence of a plant disease based on said comparison of the spectral signature with the second spectral signature;

detect a presence of wilt or leaf drop caused by environmental stressors based on said comparison of the spectral signature with the second spectral signature;

or

identify a pest, including a disease vector, and predict said pest'"'"'s trajectory.

10. The plant analysis system of claim 1, wherein a library of plant records is further configured to store historical data associated with the plant, said historical data comprising one or more of date of planting, root stock, grating record, nutritional treatment, health treatment, yield, surrounding soil conditions, surrounding atmospheric conditions, and surrounding topography.

11. The plant analysis system of claim 1, further comprising an asset management dashboard for accessing, viewing, analyzing and controlling a library of plant records.

12. The plant analysis system of claim 11, wherein said asset management dashboard integrates spatial information of a subset of said plant records in a configurable display, said subset of said plant records correspond to a plot on an orchard, and wherein said asset management dashboard allows users to generate counting and statistical reports about said plant records.

13. The plant analysis system of claim 11, wherein the processor is further configured to create a record of said plant in a library of plant records, and the record of said plant associates said plant with said spectral signature, said plant color, said spectral data, said assembled point cloud data, and said morphological data.

14. A method for analyzing a plant, comprising:

measure, using a data acquisition component coupled to a transport vehicle, properties of a plant utilizing waveform light detection and ranging (LiDAR) to assemble point cloud data, wherein said point cloud data comprises a plurality of three dimensional vertices each of which represents an external surface associated with said plant;

collect, using the data acquisition component, photographic data of said plant;

collect, using the data acquisition component, spectral data for said plant;

geo-register the point cloud data and photographic data;

associate a plurality of said three dimensional vertices of said point cloud data with global positioning satellite (GPS) data, said photographic data, and said spectral data;

determine, using one or more processors, a spectral signature of said plant based on said spectral data;

determine, using the one or more processors, plant color based on said photographic data;

associate said point cloud data with said plant color; and

generate, using the one or more processors, morphological data of said plant based on said point cloud data, said morphological data comprising one or more of plant stem diameter, plant height, plant volume, and plant leaf density.

15. The method of claim 14, wherein:

said morphological data is generated by segmenting said point cloud data to identify boundaries of said plant;

or

said morphological data is classified to identify a plant feature, said plant feature comprising a branching structure, trunk, biomass, canopy, fruit, blossom, fruit cluster, or blossom cluster.

16. The method of claim 14, wherein said point cloud data and said plant color is utilized to discriminate a fruit, blossom, fruit cluster, or blossom cluster from a shadow based, at least in part, on analyzing a variation of said plant color and a geometric shape defined by the three dimensional vertices of said point cloud data.

17. The method of claim 14, further comprising:

measuring atmospheric conditions; and

determining a phenotype of the plant based on said spectral signature, said morphological data, and said atmospheric conditions.

18. The method of claim 14, further comprising:

determining a number and a size of fruits or blossoms on the plant based one or more of said point cloud data or said plant color data, wherein said number and size of fruits or blossoms on the plant is determined by clustering said point cloud data and plant color data, and a crop yield is estimated based, at least in part, on said number and size of fruits or blossoms on the plant.

19. The method of claim 14, wherein:

the spectral signature of the plant is compared with a second spectral signature from a library of plant records to;

detect a presence of a plant disease based on said comparison of the spectral signature with the second spectral signature;

or

detect a presence of wilt or leaf drop caused by environmental stressors based on said comparison of the spectral signature with the second spectral signature.

20. The method of claim 14, further comprising:

creating a record of said plant in a library of plant records, wherein the record of said plant associates said plant with said spectral signature, said plant color, said spectral data, said assembled point cloud data, and said morphological data.