Real-time valuation and risk management interface for commodity-based operations

A system of providing predictions, estimates, or recommendations for a commodity-based operation comprising:

• a data collection module, the data collection module configured to obtain or collect information from a plurality of data inputs,
• wherein the plurality of data inputs include at least one of field information, satellite imagery data, yield data, market price data, weather forecast data, enterprise data, or asset insurance data;
• a data processing unit, the data processing unit configured to analyze or process the information from the plurality of data inputs to determine correlations, patterns, or trends in the plurality of data inputs,
• wherein the data processing unit utilizes at least one machine learning algorithm to determine correlations, patterns, or trends associated with determining at least one crop yields, or risks associated with at least one parcel of land;
• a recommendation engine, the recommendation engine configured to determine at least one of a predicted asset yield, a predicted risk metric, or a predicted expected revenue for at least one commodity-based operation;
• and
• a user interface, the user interface configured to display the determined at least one of the predicted asset yield, the predicted risk metric, or the predicted expected revenue for at least one commodity-based operation.

A system for real-time asset management and optimization, comprising:

• a controller including at least one processor and at least one memory device storing computer-readable instructions,
• wherein the controller is implemented as a distributed computing system comprising a web server tier for handling user interface requests;
• an application server tier for executing application logic and recommendation algorithms;
• and
• a data storage tier including at least one relational database and at least one time-series database optimized for sensor data storage,
• wherein the at least one processor configured to execute the computer-readable instructions to:
• collect historical data or real-time data from at least one data source, including at least one of historical field information, satellite imagery data, yield data, market price data, weather forecast data, enterprise data, or asset insurance data;
• train at least one machine learning model to determine at least one asset yield prediction, at least one revenue, at least one profit, or at least one assessed risk associated with at least one parcel of land or at least one commodity using a first training data set that includes the historical data;
• receive, from a user interface, one or more user selections corresponding to at least one commodity-based operation and a risk threshold for the at least one commodity-based operation;
• responsive to receiving the one or more user selections, select one or more trained machine learning models;
• determine at least one of a predicted asset yield, a predicted risk metric, or a predicted expected revenue for the at least one commodity-based operation based at least in part on an analysis of the machine learning model, the at least one commodity-based operation, and the risk threshold;
• cause at least one of the predicted asset yield, the risk metric, or the expected revenue for the at least one commodity-based operation to be displayed.