Highly responsive farming systems with extraordinary in-season optimization

1. A method for controlling application of agrichemical products, comprising:

acquiring remotely sensed digital image data, comprising:

receiving a first set of data from a hotspot remote imagery subsystem;

generating a set of diagnostic instructions based on the first set of data; and

using the set of diagnostic instructions, remotely controlling a diagnostic scouting subsystem on a scouting mission to obtain a second set of data;

developing a prescription to apply at least one agrichemical product in a variable manner based on at least the digital image data, wherein the prescription describes a plurality of passes of a particular autonomous vehicle over a field to apply the at least one agrichemical product;

applying the at least one agrichemical product to a crop in the variable manner by the particular autonomous vehicle according to the prescription.

2. The method of claim 1 , wherein the particular autonomous vehicle is an unmanned aerial vehicle.

3. The method of claim 1 , wherein the prescription describes a mode of action that is different for at least one of the plurality of passes of the particular autonomous vehicle over the field to apply the at least one agrichemical product.

4. The method of claim 1 , wherein the prescription describes a category of one of the at least one agrichemical product that is different for at least one of the plurality of passes of the particular autonomous vehicle over the field to apply the at least one agrichemical product.

5. The method of claim 1 , wherein the prescription describes reapplication of the agrichemical product in a subsequent pass of the particular autonomous vehicle over the field, the subsequent pass occurring after a first pass of the plurality of passes of the particular autonomous vehicle over the field.

6. The method of claim 1 , wherein the prescription is developed based on historical data from at least one prior growing season.

7. The method of claim 1 , wherein the prescription is developed based on forecast weather events.

8. The method of claim 1 , wherein the prescription comprises a pest control prescription, wherein the prescription is developed prior to and the agrichemical product is applied prior to a detection of pest or a detection of symptoms of pest within the field.

9. The method of claim 1 , wherein the prescription comprises a pre-emergent prescription, wherein the prescription is developed prior to and the agrichemical product is applied prior to a detection of weed within the field.

10. The method of claim 1 , wherein the agrichemical product includes an herbicide, an insecticide, a fungicide, a microbial, a micronutrient, a nitrogen-based fertilizer, a plant growth regulator, a defoliant, a soil amendment, or a combination thereof.

11. The method of claim 1 , wherein the first set of data includes first multi-spectral images at a first resolution, and the second set of data includes second multi-spectral images at a second resolution that is higher than the first resolution.

12. The method of claim 11 , wherein at least one set of multi-spectral images comprises hyperspectral images.

13. The method of claim 11 , wherein the second set of data is obtained at a lower altitude than the first set of data.

14. A method for controlling application of agrichemical products, comprising:

acquiring remotely sensed digital image data;

obtaining historical data from at least one prior growing season;

developing a prescription to apply at least one agrichemical product in a variable manner based on the digital image data and the historical data, wherein the prescription describes a plurality of passes of a particular autonomous vehicle over a field to apply the at least one agrichemical product;

applying the at least one agrichemical product to a crop in the variable manner by the particular autonomous vehicle according to the prescription.

15. The method of claim 14 , wherein the historical data comprises at least one of:

past prescriptions and corresponding prescription results, from a previous growing season prior to a current growing season;

weed species and density from the previous growing season;

insect damage from the previous growing season;

disease severity from the previous growing season;

soil data from the previous growing season; and

yield information from the previous growing season.

16. The method of claim 14 , wherein said historical data comprises spatial maps derived from imagery data using artificial intelligence or machine learning algorithms.

17. The method of claim 14 , wherein the prescription describes a mode of action that is different for at least one of the plurality of passes of the particular autonomous vehicle over the field to apply the at least one agrichemical product.

18. The method of claim 14 , wherein the prescription describes a category of one of the at least one agrichemical product that is different for at least one of the plurality of passes of the particular autonomous vehicle over the field to apply the at least one agrichemical product.

19. The method of claim 14 , wherein acquiring the remotely sensed digital image data comprises:

receiving a first set of data from a hotspot imagery subsystem;

generating a set of diagnostic instructions based on the first set of data;

using the set of diagnostic instructions, remotely controlling a diagnostic scouting subsystem on a scouting mission to obtain a second set of data.