Abstract: A memory embodies instructions, and a processor is coupled to the memory and is operative by the instructions to facilitate: accessing a source of information regarding farm cultivation techniques; constructing a cultivation knowledge graph by parsing the source of information regarding farm cultivation techniques, using natural language processing; identifying cultivation quality assessment factors by applying machine learning to the cultivation knowledge graph; estimating quality of a farm cultivation task by comparing a stream of real-time data to the cultivation quality assessment factors, wherein the stream of real-time data is related to performance of the farm cultivation task; identifying from the stream of real-time data, using the cultivation knowledge graph, a controllable variable that affects the quality of the farm cultivation task; and improving the quality of the farm cultivation task by facilitating a change in the controllable variable.

Abstract: Methods and systems for buyback provisioning. A risk level for each of a plurality of items at risk of losing market value is computed and the plurality of items at risk of losing market value are differentiated based on the corresponding risk levels. An identity of candidate substitute items is learned by communicating with a distribution system and one or more of the candidate substitute items to exchange for one of the plurality of items are identified based on one or more contextual factors. An exchange of the item and at least one of the candidate substitute items is facilitated, the facilitating comprising highlighting the item on a graphical user interface to indicate that the item is losing value or is at risk of losing value.

Abstract: A memory embodies instructions, and a processor is coupled to the memory and is operative by the instructions to facilitate: accessing a source of information regarding farm cultivation techniques; constructing a cultivation knowledge graph by parsing the source of information regarding farm cultivation techniques, using natural language processing; identifying cultivation quality assessment factors by applying machine learning to the cultivation knowledge graph; estimating quality of a farm cultivation task by comparing a stream of real-time data to the cultivation quality assessment factors, wherein the stream of real-time data is related to performance of the farm cultivation task; identifying from the stream of real-time data, using the cultivation knowledge graph, a controllable variable that affects the quality of the farm cultivation task; and improving the quality of the farm cultivation task by facilitating a change in the controllable variable.

Abstract: Methods and systems for buyback provisioning. A risk level for each of a plurality of items at risk of losing market value is computed and the plurality of items at risk of losing market value are differentiated based on the corresponding risk levels. An identity of candidate substitute items is learned by communicating with a distribution system and one or more of the candidate substitute items to exchange for one of the plurality of items are identified based on one or more contextual factors. An exchange of the item and at least one of the candidate substitute items is facilitated, the facilitating comprising highlighting the item on a graphical user interface to indicate that the item is losing value or is at risk of losing value.