Method and apparatus for remote monitoring and management of storage using machine learning and data analytics

A system for monitoring grain stored in a storage environment comprising:

• two or more sensor units comprising a first sensor unit and a second sensor unit located in a grain storage environment, wherein each of the two or more sensor units is configured to detect an environmental variable associated with the grain storage environment, generate environmental data based on the detected environmental variable, and transmit the environmental data;
• and
• a cloud computing system configured to receive the environmental data from the two or more sensor units, analyze the environmental data, and generate an output based on the analysis of the environmental data;
• wherein the first sensor unit is configured to transmit environmental data generated by the first sensor unit to the second sensor unit via a first network, and wherein the second sensor unit is configured to transmit at least the environmental data generated by the first sensor unit and environmental data generated by the second sensor unit to the cloud computing system via a second network;
• wherein the cloud computing system is configured to store the environmental data received from the two or more sensor units over time, thereby generating a historical environmental data set;
• wherein the cloud computing system is configured to apply an artificial-intelligence trained algorithm to the historical environmental data set, thereby generating a predictive model based on an initial GSQI value and the historical environmental data set;
• wherein the environmental data comprises data corresponding to at least one of the following environmental values:
• a temperature value, a humidity value, an oxygen value, a carbon dioxide value, a phosphine value, an ethylene value, and a sound or acoustic value;
• wherein the output comprises a prediction of a current GSQI value derived from the predictive model and corresponding to the quality of the grain in the grain storage environment, wherein the predicted current GSQI value is generated automatically upon receipt of updated environmental data;
• wherein the cloud computing system is configured to automatically initiate a downstream process based on the predicted current GSQI value by transmitting provisioning data to an actuator configured to engage a mechanical process, the mechanical process comprising at least one of adjusting one or more fans, adjusting one or more vents, stirring the grain, and adjusting fumigation parameters;
• and
• wherein the cloud computing system is configured to update the historical environmental data set and the predictive model as the cloud computing system receives updated environmental data.