Granted Patent B2

US 12,501,848 · App. 18/742,407 · Granted Dec 23, 2025

Inventors: Joe Holoubek (Ames, IA); Chad Swindoll (Sumner, MO); Aaron Friedlein (Farmersburg, IA); Roger Zielke (Huxley, IA)

Assignee: Ag Leader Technology

A01C7/105A01G24/20G06Q10/06315G06Q50/02

Abstract

Claims (37)

1 . An on-the-go planting system comprising:

(a) a soil sensor configured to assess a soil quality; and

(b) a processor configured to weight two or more field attributes to determine a yield potential, the field attributes comprising:

(i) the soil quality;

(ii) normalized yield data;

(iii) terrain data; and

(iv) geo-spatial data,

wherein in the system is configured to prescribe one or more of a seed variety, a seeding rate, and a soil treatment based on the yield potential.

2 . The on-the-go planting system of claim 1 , wherein the soil quality is soil organic matter data.

3 . The on-the-go planting system of claim 1 , wherein terrain data comprises one or more of elevation data, soil type, drainage intensity, landscape positions, slope, grade, and wetness potential.

4 . The on-the-go planting system of claim 1 , wherein the soil sensor is an optical sensor.

5 . The on-the-go planting system of claim 1 , wherein the soil sensor is disposed on a planter row unit.

6 . The on-the-go planting system of claim 1 , further comprising a display housing the processor configured to display field attributes to a user.

7 . The on-the-go planting system of claim 1 , wherein the processor comprises an artificial intelligence for weighting.

8 . The on-the-go planting system of claim 1 , further comprising an artificial intelligence executed on the processor.

9 . The on-the-go planting system of claim 1 , wherein weights for the two or more field attributes are automatically adjusted by the system.

10 . A method for prescribing a planting prescription comprising:

sensing organic matter data in an open trench via a soil sensor;

weighting sensed organic matter data with at least one field attribute, field attributes comprising: normalized yield data, terrain data, geo-spatial data, and user input data;

determining a yield potential from the weighted sensed organic matter data and at least one field attribute; and

outputting a prescription including one or more of seed variety, variable seeding rate, and soil treatment.

11 . The method of claim 10 , further comprising displaying on a display a map depicting organic matter data and the prescription.

12 . The method of claim 10 , further comprising an artificial intelligence executed on a processor for weighting sensed organic matter data with at least one field attribute.

13 . The method of claim 10 , further comprising an artificial intelligence executed on a processor for determining a yield potential from the weighted sensed organic matter data and at least one field attribute.

14 . The method of claim 10 , wherein the at least one field attribute is selected from normalized yield data, terrain data, geo-spatial data, and user input data.

15 . The method of claim 10 , wherein the at least one field attribute is terrain data.

16 . The method of claim 15 , wherein terrain data comprises one or more of elevation data, soil type, drainage intensity, landscape positions, slope, grade, and wetness potential.

17 . The method of claim 10 , further comprising assigning weights to the sensed organic matter data and the at least one field attribute.

18 . The method of claim 17 , wherein weights assigned to the sensed organic matter data and the at least one field attribute are adjusted automatically.

19 . An on-the-go prescription planting system comprising:

(a) a soil sensor configured to detect organic matter in a seed trench; and

(b) a processor in communication with the soil sensor, the processor configured to:

(i) assign a weight to and weight organic matter data from the soil sensor and terrain data;

ii determine a yield potential based on the weighted weight organic matter data and terrain data;

(iii) prescribe a seeding rate; and

(iv) adjust a seed meter to apply the prescribed seeding rate.

20 . The on-the-go prescription planting system of claim 19 , wherein the weights assigned to the organic matter data and terrain data are adjusted via machine learning.

Continuity (3)

References Cited (400)