A system for detecting the levelness of ground engaging tools of a tillage implement including an agricultural implement including a frame and tool assemblies supported relative to the frame. The tool assemblies each include a toolbar coupled to the frame and one or more ground engaging tools coupled to the toolbar. The system further includes sensors coupled to two of the tool assemblies and configured to capture data indicative of a load acting on the one or more ground engaging tools of the tool assemblies. Additionally, the system includes a controller configured to monitor the data received from the sensors and compare at least one monitored value associated with the load acting on the ground engaging tool(s) of each of the tool assemblies. Moreover, the controller is further configured to identify the ground engaging tool(s) are not level when the monitored value(s) differ by a predetermined threshold value.

A system for detecting the levelness of ground engaging tools of a tillage implement including an agricultural implement including a frame and tool assemblies supported relative to the frame. The tool assemblies each include a toolbar coupled to the frame and one or more ground engaging tools coupled to the toolbar. The system further includes sensors coupled to two of the tool assemblies and configured to capture data indicative of a load acting on the one or more ground engaging tools of the tool assemblies. Additionally, the system includes a controller configured to monitor the data received from the sensors and compare at least one monitored value associated with the load acting on the ground engaging tool(s) of each of the tool assemblies. Moreover, the controller is further configured to identify the ground engaging tool(s) are not level when the monitored value(s) differ by a predetermined threshold value.

Adaptive modification and presentment of user interface elements in a computerized therapeutic treatment regimen. Embodiments of the present disclosure provide for non-linear computational analysis of cData and nData derived from user interactions with a mobile electronic device executing an instance of a computerized therapeutic treatment regimen. The cData and nData may be computed according to one or more artificial neural network or deep learning technique to derive patterns between computerized stimuli or interactions and sensor data. Patterns derived from analysis of the cData and nData may be used to define an effort metric associated with user input patterns in response to the computerized stimuli or interactions being indicative of a measure of user engagement or effort. A computational model or rules engine may be applied to adapt, modify, configure or present one or more graphical user interface elements in a subsequent instance of the computerized therapeutic treatment regimen.