Irrigation controller using regression model

• US 20030109964A1
• Filed: 11/07/2002
• Published: 06/12/2003
• Est. Priority Date: 12/11/2001
• Status: Abandoned Application

• Current Assignee: John Addink, Sylvan Addink
• Original Assignee: John Addink, Sylvan Addink
• Inventors: Addink, Sylvan, Addink, John
• Application Number:
  US10/291,100
• Publication Number:
  US 20030109964A1
• Time in Patent Office: Days
• US Class Current: 700/284
• CPC Class Codes: A01G 25/16 Control of watering

Abstract

The present invention provides systems and methods in which an irrigation controller uses a regression model to estimate an evapotranspiration rate (estimated ETo), and uses the estimated ETo to affect an irrigation schedule executed by the controller. The regression model is preferably based upon a comparison of historical ETo values against corresponding historical environmental values, with the data advantageously spanning a time period of at least one month, and more preferably at least two months. Data for multiple environmental factors may also be used. The environmental factor(s) utilized may advantageously comprise one or more of temperature, solar radiation, wind speed, humidity, barometric pressure, cloud cover and soil moisture.

Claims

1. An irrigation controller comprising:

a memory that stores a regression model;

a microprocessor that applies a current value for an environmental factor to the regression model to estimate a current evapotranspiration rate (estimated ETo); and

a mechanism that uses the estimated ETo to affect an irrigation schedule executed by the controller.

2. The controller of claim 1 wherein the regression model is based at least in part upon a set of historical ETo values and a set of corresponding historical values for the environmental factor.

3. The controller of claim 2 wherein the set of historical ETo values spans a time period of at least two days.

4. The controller of claim 2 wherein the regression model is further based upon a second set of historical values for a second environmental factor.

5. The controller of claim 1 wherein the regression model comprises a linear regression.

6. The controller of claim 1 wherein the regression model comprises a multiple regression.

7. The controller of claim 1 wherein the environmental factor is at least one of temperature, solar radiation, wind speed, humidity, barometric pressure, cloud cover, and soil moisture.

8. The controller of claim 7 wherein the temperature is air temperature.

9. The controller of claim 7 wherein the temperature is soil temperature.

10. The controller of claim 1 wherein the current value applied by the microprocessor is limited by at least one of a set maximum value and a set, non-zero, minimum value.

11. The controller of claim 1, further comprising the mechanism using an irrigation efficiency value to at least partly affect the irrigation schedule executed by the controller.

12. The controller of claim 1, further comprising the mechanism using a crop coefficient value to at least partly affect the irrigation schedule executed by the controller.