Systems and methods for predictive irrigation system maintenance

A computer-implemented method for predictive maintenance for an irrigation system, the computer-implemented method comprising:

• receiving a first signal, sensed by a first sensor disposed at a main electrical disconnect of an electrical utility, indicative of a condition of at least one component of a plurality of components of the irrigation system based on network power quality for at least two components of the plurality of components, the irrigation system configured to irrigate a farming area and including the plurality of components, wherein the first sensor includes a current sensor, a power sensor, a voltage sensor, or combinations thereof;
• determining changes in the condition of an individual component of the plurality of components of the irrigation system based on the first signal;
• receiving a second signal from a second sensor, wherein the second sensor includes an encoder, a pressure sensor, a flow meter, or combinations thereof,
• determining changes in the condition of the individual component based on the second signal;
• predicting, by a machine learning model, an amount of unexpected downtime of the individual component based on predetermined data and the determined changes in the condition of the individual component, wherein the machine learning model includes at least two of:
• an endgun prediction model configured to model movement of an endgun of the irrigation system;
• a tower drive prediction model configured to predict which tower of a plurality of towers of the irrigation system moves based on a power surge sequence;
• or
• a sequencing prediction model configured to model a state of solenoids and pressure in the irrigation system;
• and
• effectuating repair of the individual component in response to the prediction.

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method for predictive maintenance for an irrigation system, the method comprising:

• receiving a first signal, sensed by a first sensor disposed at a main electrical disconnect of an electrical utility, indicative of a condition of at least one component of a plurality of components of the irrigation system based on network power quality for at least two components of a plurality of components of the irrigation system, the irrigation system configured to irrigate a farming area and including the plurality of components, wherein the first sensor includes a current sensor, a power sensor, a voltage sensor, or combinations thereof;
• determining changes in the condition of an individual component of the plurality of components based on the first signal;
• receiving a second signal from a second sensor, wherein the second sensor includes an encoder, a pressure sensor, a flow meter, or combinations thereof,
• determining changes in the condition of the individual component based on the second signal;
• predicting, by a machine learning model, an amount of unexpected downtime of the one component based on predetermined data and the determined changes in the condition of the individual component, wherein the machine learning model includes at least two of:
• an endgun prediction model configured to model movement of an endgun of the irrigation system;
• a tower drive prediction model configured to predict which tower of a plurality of towers of the irrigation system moves based on a power surge sequence;
• or
• a sequencing prediction model configured to model a state of solenoids and pressure in the irrigation system;
• and
• effectuating repair of the individual component in response to the prediction.