SYSTEMS AND METHODS FOR PREDICTING REGIONAL TURFGRASS PERFORMANCE

• US 20140100817A1
• Filed: 10/05/2012
• Published: 04/10/2014
• Est. Priority Date: 10/05/2012
• Status: Active Grant

• Current Assignee: Heritage Landscape Supply Group, Inc. (SRS Group Holdings, Inc.)
• Original Assignee: Land O'Lakes Incorporated
• Inventors: Jump, Bruce A.
• Granted Patent: US 9,087,022 B2
• Time in Patent Office: Days
• US Class Current: 702/180
• CPC Class Codes: A01G 20/00 Cultivation of turf, lawn or the like; Apparatus or methods therefor hand-held edge trimmers or shears for lawn A01G3/06; edging for lawn A01G9/28; lawn-mowers A01D34/00
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06Q 50/02 Agriculture; Fishing; Forestry; Mining

Abstract

Grass seed performance may be predicted by receiving grass seed selections for individual grass seed varieties and target geographic regions for growing the grass seed selection. A common set of grass attributes is identified from historical grass attribute values associated with the selected target regions. The historical grass attribute values in the first common set of grass attributes are retrieved and are displayed in a graphical format for the at least two individual grass seed varieties in the at least two selected target regions. A weighted average may be calculated for the historical grass attribute values in the common set and may provide a prediction of performance of the individual grass seed varieties across the selected geographic regions or in regions proximate the selected geographic regions.

Claims

1. A computer-implemented method for predicting performance of grass seed varieties within geographic regions, the method comprising:

using a computer database to store a set of grass attributes and historical grass attribute values for a plurality of individual grass seed varieties, wherein a number and a type of historical grass attribute values from the set of grass attributes differs among the plurality of individual grass seed varieties, wherein for the individual grass seed varieties, the historical grass attribute values include a geographic region in which the grass seeds were grown, and wherein the historical grass attribute values for the plurality of individual grass seed varieties are associated with a plurality of geographic regions;

using a computer processor to;

receive a grass seed selection of at least two individual grass seed varieties and at least two target geographic regions for growing the grass seed selection;

identify a common set of grass attributes from the historical grass attribute values for the selected target regions from the differing number and type of historical grass attributes stored in the database;

retrieve the historical grass attribute values in the common set of grass attributes; and

display the common set of retrieved historical grass attribute values in a graphical format for the at least two individual grass seed varieties in the at least two selected target regions as a prediction of performance of the individual grass seed varieties within the individual regions.

2. The method of claim 1, further comprising using the computer processor to:

calculate a weighted average of the historical grass attribute values in the common set for the at least two individual grass seed varieties based on the at least two selected target regions; and

display the weighted average for the historical grass attribute values in a graphical format as a prediction of performance of the individual grass seed varieties across the at least two geographic regions.

3. The method of claim 2, wherein the weighted average is displayed with the historical grass attribute values in the graphical format.

4. The method of claim 1, further comprising using the computer processor to receive a selection of the at least one of the grass seed varieties by historical growing year, and wherein the processor is used to identify the common set of historical grass attributes for the growing year in the selected target regions.

5. The method of claim 1, further comprising using the computer processor to display a statistical benchmark value for each of the grass attributes in the common set of attributes thereby providing a graphical indicator of statistically significant differences in the grass attribute values among the individual grass seed varieties.

6. The method of claim 1, wherein the grass attributes stored using the computer database comprise National Turfgrass Evaluation Program (NTEP) grass attributes.

7. The method of claim 1, further comprising using the computer processor to:

receive a themed selection, the themed selection representing a group of attributes having commonalities; and

identify the common set of attributes from the themed selection.

8. A computer-implemented method for predicting performance of individual seed varieties across geographic regions, the method comprising:

using a computer database to store a set of grass attributes and historical grass attribute values for a plurality of individual grass seed varieties, wherein a number and a type of historical grass attribute values from the set of grass attributes differs among the plurality of individual grass seed varieties, wherein for the individual grass seed varieties, the historical grass attribute values include a geographic region in which the individual grass seed varieties were grown, and wherein the historical grass attribute values for the plurality of individual grass seed varieties are associated with a plurality of geographic regions;

using a computer processor to;

receive a grass seed selection of at least two individual grass seed varieties, at least two historical growing years, and at least two target geographic regions for growing the grass seed selection;

identify a common grass attribute from the historical grass attribute values for the selected target regions for each of the historical growing years;

retrieve the historical grass attribute values for the common grass attribute; and

display the retrieved historical grass attribute values for the identified common grass attribute in a graphical format as a prediction of attribute performance of the individual grass seed varieties within the individual regions.

9. The method of claim 8, further comprising displaying a statistical benchmark value for the grass attribute thereby providing a graphical indicator of statistically significant differences in the grass attribute value among the individual grass seed varieties.

10. The method of claim 8, wherein the grass attributes stored using the computer database comprise National Turfgrass Evaluation Program (NTEP) grass attributes.

11. A computer-implemented method for predicting performance of individual grass seed varieties within a geographic region, the method comprising:

using a computer database to store grass attributes and historical grass attribute values for a plurality of individual grass seed varieties, wherein a number and a type of grass attributes differs among the plurality of individual grass seed varieties, wherein for the individual grass seed varieties, the historical grass attribute values include a geographic region in which the grass seeds were grown, and wherein the historical grass attribute values for the plurality of individual grass seed varieties are associated with a plurality of geographic regions;

using a computer processor to;

receive a first selection of at least two grass seeds and at least two target geographic regions for growing the grass seed selection, wherein the at least two target geographic regions are proximate a third geographic region;

identify a first common set of grass attributes from the historical grass attribute values for the selected target regions from the differing number and type of grass attributes stored in the database;

retrieve the historical grass attribute values for the common grass attribute;

calculate a first weighted average in the common set for each of the retrieved historical grass attribute values; and

display the first weighted average for the historical grass attribute values in a graphical format as a prediction of performance of the individual grass seed varieties in the third geographic region.

12. The method of claim 11, further comprising using the computer processor to receive a selection of the at least two grass seeds by historical growing year, and wherein the processor is used to identify the common set of historical grass attributes for the selected year in the selected target region.

13. The method of claim 11, wherein the grass attributes stored using the computer database comprise NTEP grass attributes.

14. The method of claim 11, wherein the plurality of geographic regions stored using the computer database comprises one or more of a plurality of states and a plurality of regions within the plurality of states.

15. The method of claim 11, further comprising using the computer processor to:

receive a second selection of at least two target regions for the at least two grass seeds, wherein the second selection includes a target region proximate one of the target geographic regions from the first selection;

identify a second common set of grass attributes from the historical grass attribute values for the selected target regions;

calculate a second weighted average for each of the historical grass attribute values in the second common set based on the second selection; and

display the second weighted average for the historical grass attribute values in the graphical format with the first weighted average as predictions of grass seed performance across the target regions in the second selection.

16. The method of claim 15, further comprising using the computer processor to identify at least one attribute for including in the second common set of attributes that is the same as an attribute from the first common set of attributes.

17. The method of claim 11, further comprising using the computer processor to:

receive a second selection of at least two grass seeds for the at least two target regions, wherein the grass seeds of the second selection differ from the first selection;

identify a second common set of grass attributes from the historical grass attribute values for the selected target regions;

calculate a second weighted average for each of the historical grass attribute values in the second common set based on the second selection; and

display the second weighted average for the historical grass attribute values in the graphical format with the first weighted average as predictions of grass seed performance in the third geographic region.

18. The method of claim 17, further comprising using the computer processor to identify at least one attribute for including in the second common set of attributes that is the same as an attribute from the first common set of attributes.