Abstract: A framework for modeling performance of bin systems in precision agriculture settings, and grain stored in containers, collects information on auxiliary devices, the grain, and the container using both wireless sensors built into devices and transient sensors that are embedded within and move with the grain. Machine learning-based models are developed and applied within this framework for performing management functions such as monitoring for failure or malfunction, and controlling and adjusting bin systems in response to sensed conditions. Information collected and processed within this framework is stored and tokenized in a secure ledger for tracking, transactions, verification, and authentication of crop value and carbon emission-related data.

Abstract: A post-harvest crop management platform is provided for regulating conditions of an agricultural crop being dried and/or stored. The platform utilizes data collected from sensors positioned proximate to, or embedded within, an agricultural crop, and analyzes selected crop characteristics affecting the stored crop in multiple sections thereof. The platform identifies parameters relative to achieving a desired crop characteristic level in the agricultural crop, generates a profile of the selected crop characteristic across the multiple sections of the stored crop, and models an application of a fluid flow pattern to achieve the desired crop characteristic level in each section. The crop storage monitoring and management platform also actuates a multi-stack assembly, configured within the stored crop, to automatically apply the fluid flow pattern in one or more cycles that are adjustable to changing conditions within the stored crop in real time.

Abstract: A post-harvest crop management platform is provided for regulating conditions of an agricultural crop being dried and/or stored. The platform utilizes data collected from sensors positioned proximate to, or embedded within, an agricultural crop, and analyzes selected crop characteristics affecting the stored crop in multiple sections thereof. The platform identifies parameters relative to achieving a desired crop characteristic level in the agricultural crop, generates a profile of the selected crop characteristic across the multiple sections of the stored crop, and models an application of a fluid flow pattern to achieve the desired crop characteristic level in each section. The crop storage monitoring and management platform also actuates a multi-stack assembly, configured within the stored crop, to automatically apply the fluid flow pattern in one or more cycles that are adjustable to changing conditions within the stored crop in real time.