Apparatus and method for treating a growth medium via an electrical discharge

1. An apparatus for treating a growth medium via an electrical discharge, the apparatus comprises a treatment chamber, at least a reservoir, a plasma reactor, an injector, a pressure regulation system, and an ignition unit comprising:

a voltage source configured to provide an electrical voltage;

a converter configured to convert the electrical voltage from a direct current (DC) voltage input to an alternating current (AC) voltage output; and

an electrical connection interface configured to electrically connect the converter to at least one electrode of a pair of electrodes disposed in the plasma reactor, wherein the pair of electrodes comprises a first electrode and a second electrode;

a feedback mechanism comprising a sensor configured to detect reaction data related to reactions within a reaction region disposed between the first electrode and the second electrode; and

a control module communicatively connected to the feedback mechanism, wherein the control module is configured to:

initiate a generation of an electrical discharge in the reaction region as a function of the AC voltage output, wherein the reaction region is configured to:

enable an interaction between the electrical discharge and a growth medium contained in the treatment chamber.

2. The apparatus of claim 1 , wherein the first electrode of the at least a pair of electrodes is configured to diverge from the second electrode of the at least a pair of electrodes.

3. The apparatus of claim 1 , wherein each electrode of the at least a pair of electrodes comprises a pitch angle of 12 degrees.

4. The apparatus of claim 1 , wherein the reaction region comprises a plurality of points of arc between the first electrode of the at least a pair of electrodes and the second electrode of the at least a pair of electrodes.

5. The apparatus of claim 1 , wherein the at least one electrode of the pair of electrodes comprises a dielectric insulation.

6. The apparatus of claim 1 , wherein the conversion is configured to the DC voltage input to a high-voltage discharge at 10,000 kHz (10 MHz).

7. The apparatus of claim 1 , wherein the sensor comprises at least one sensor selected from plurality of sensors consisting of a voltage sensor, a current sensor, a temperature sensor, a moisture sensor, and an optical sensor.

8. The apparatus of claim 1 , wherein the reaction data comprises a plurality of electrical discharge parameters, a plurality of fluid parameters, and a plurality of growth medium parameters.

9. The apparatus of claim 1 , wherein the control module is configured to:

receive the reaction data detected by the sensor from the feedback mechanism; and

adjust at least a treatment parameter of the apparatus as a function of the reaction data.

10. The apparatus of claim 9 , wherein adjusting the at least a treatment parameter comprises:

training a treatment machine-learning model using treatment training data, wherein the treatment training data comprises a plurality of reaction data as input correlated to a plurality of treatment parameters as output; and

determining at least a treatment parameter as a function of the trained treatment machine-learning model.

11. The apparatus of claim 1 , wherein the at least one electrode of the pair of electrodes comprises a dielectric insulation.

12. A method for treating a growth medium via an electrical discharge, the method comprises:

providing, by a voltage source, an electrical voltage;

converting, by a converter, the electrical voltage from a direct current (DC) voltage input to an alternating current (AC) voltage output;

connecting, by an electrical connection interface, the converter to at least one electrode of a pair of electrodes disposed in a plasma reactor electrically, wherein the pair of electrodes comprises a first electrode and a second electrode;

initiating, by a control module, a generation of an electrical discharge in a reaction region disposed between the first electrode and the second electrode;

enabling, by the reaction region, an interaction between the electrical discharge and a growth medium contained in the treatment chamber; and

detecting, by a feedback mechanism, reaction data using a sensor, wherein the reaction data is related to reactions within the reaction region.

13. The method of claim 12 , wherein the first electrode of the at least a pair of electrodes is configured to diverge from the second electrode of the at least a pair of electrodes.

14. The method of claim 12 , wherein each electrode of the at least a pair of electrodes comprises a pitch angle of 12 degrees.

15. The method of claim 12 , wherein the reaction region comprises a plurality of points of arc between the first electrode of the at least a pair of electrodes and the second electrode of the at least a pair of electrodes.

16. The method of claim 12 , wherein the converter is configured to the DC voltage input to a high-voltage discharge at 10,000 kHz (10 MHz).

17. The method of claim 12 , wherein the sensor comprises at least one sensor selected from a plurality of sensors consisting of a voltage sensor, a current sensor, a temperature sensor, a moisture sensor, and an optical sensor.

18. The method of claim 12 , wherein the reaction data comprises a plurality of electrical discharge parameters, a plurality of fluid parameters, and a plurality of growth medium parameters.

19. The method of claim 12 , wherein the method further comprises steps of:

receiving, by the control module, the reaction data detected by the sensor from the feedback mechanism; and

adjusting, by the control module, at least a treatment parameter of the apparatus as a function of the reaction data.

20. The method of claim 19 , wherein adjusting the at least a treatment parameter comprises:

training, by the control module, a treatment machine-learning model using treatment training data, wherein the treatment training data comprises a plurality of reaction data as input correlated to a plurality of treatment parameters as output; and

determining, by the control module, at least a treatment parameter as a function of the trained treatment machine-learning model.