A predictive maintenance system for an irrigation system including a drive system, the predictive maintenance system comprising: at least one sensor configured to measure an amount of a reactive power and generate a signal indicative of abnormal operation within the irrigation system, the sensor electrically coupled to the drive system; a processor; and a memory, including instructions stored thereon, which when executed by the processor cause the predictive maintenance system to: receive the generated signal; determine an abnormal operation of the drive system based on the generated signal, wherein the abnormal operation is a drive system anomaly due to a deflated or flattened tire of the drive system; and predict, by a machine learning model, a maintenance requirement of the drive system based on the determined abnormal operation, wherein the maintenance requirement is predicted by: generating, based on the received sensor signal, a data structure that is formatted to be processed through one or more layers of a machine learning model, wherein the data structure includes one or more fields structuring data; processing data that includes the data structure, through each of the one or more layers of the machine learning model that have been trained to predict a likelihood that a particular piece of the drive system may require maintenance; and generating, by an output layer of the machine learning model, an output data structure, wherein the output data structure includes one or more fields structuring data indicating a likelihood that a particular piece of equipment may require maintenance. at least one sensor configured to measure an amount of a reactive power and generate a signal indicative of abnormal operation within the irrigation system, the sensor electrically coupled to the drive system; a processor; and a memory, including instructions stored thereon, which when executed by the processor cause the predictive maintenance system to: receive the generated signal; determine an abnormal operation of the drive system based on the generated signal, wherein the abnormal operation is a drive system anomaly due to a deflated or flattened tire of the drive system; and predict, by a machine learning model, a maintenance requirement of the drive system based on the determined abnormal operation, receive the generated signal; determine an abnormal operation of the drive system based on the generated signal, wherein the abnormal operation is a drive system anomaly due to a deflated or flattened tire of the drive system; and predict, by a machine learning model, a maintenance requirement of the drive system based on the determined abnormal operation, wherein the maintenance requirement is predicted by: generating, based on the received sensor signal, a data structure that is formatted to be processed through one or more layers of a machine learning model, wherein the data structure includes one or more fields structuring data; processing data that includes the data structure, through each of the one or more layers of the machine learning model that have been trained to predict a likelihood that a particular piece of the drive system may require maintenance; and generating, by an output layer of the machine learning model, an output data structure, wherein the output data structure includes one or more fields structuring data indicating a likelihood that a particular piece of equipment may require maintenance. generating, based on the received sensor signal, a data structure that is formatted to be processed through one or more layers of a machine learning model, wherein the data structure includes one or more fields structuring data; processing data that includes the data structure, through each of the one or more layers of the machine learning model that have been trained to predict a likelihood that a particular piece of the drive system may require maintenance; and generating, by an output layer of the machine learning model, an output data structure, wherein the output data structure includes one or more fields structuring data indicating a likelihood that a particular piece of equipment may require maintenance.