An embodiment includes a threshing system of an agricultural harvester. The threshing system including a rotor cage surrounding a rotor defining a threshing space there between, where the rotor cage has a cut crop entrance, a transition cone defining an infeed to said rotor cage, where the transition cone is positioned to direct crop flow toward the cut crop entrance of the rotor cage, and an infeed ramp positioned between the rotor cage and the transition cone, where the infeed ramp includes guide vanes for guiding the crop flow from the transition cone into the cut crop entrance of the rotor cage.

An embodiment includes a threshing system of an agricultural harvester. The threshing system including a rotor cage surrounding a rotor defining a threshing space there between, where the rotor cage has a cut crop entrance, a transition cone defining an infeed to said rotor cage, where the transition cone is positioned to direct crop flow toward the cut crop entrance of the rotor cage, and an infeed ramp positioned between the rotor cage and the transition cone, where the infeed ramp includes guide vanes for guiding the crop flow from the transition cone into the cut crop entrance of the rotor cage.

A chopping assembly is configured to receive crop plants processed by an agricultural combine harvester. The chopping assembly includes an inlet and a plurality of blades rotatable about an axis. The plurality of blades is positioned downstream of the inlet. The chopping assembly also includes an outlet positioned downstream of the plurality of blades. The outlet is partially defined by a first wall and a second wall. The chopping assembly further includes an actuator coupled to the second wall. The actuator is configured to move the second wall relative to the first wall. A control processor is in communication with the actuator. The control processor is configured to receive a first signal corresponding to a condition representative of a characteristic of the crop plants. The control processor is also configured to generate a second signal operable to control the actuator based on the condition.

A chopping assembly is configured to receive crop plants processed by an agricultural combine harvester. The chopping assembly includes an inlet and a plurality of blades rotatable about an axis. The plurality of blades is positioned downstream of the inlet. The chopping assembly also includes an outlet positioned downstream of the plurality of blades. The outlet is partially defined by a first wall and a second wall. The chopping assembly further includes an actuator coupled to the second wall. The actuator is configured to move the second wall relative to the first wall. A control processor is in communication with the actuator. The control processor is configured to receive a first signal corresponding to a condition representative of a characteristic of the crop plants. The control processor is also configured to generate a second signal operable to control the actuator based on the condition.

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.

A system and method of controlling an agricultural machine uses the following steps: (a) successively recording signals from at least one first sensor sensing at least one agronomic parameter of a field during an operation of the machine in the field, (b) successively recording signals from at least one second sensor sensing at least one operation parameter of the machine during the operation of the machine in the field, (c) spatially overlaying the signals of the first sensor and the second sensor, (d) determining from the overlaid values a respective zone in the field, and (e) controlling an actuator of the machine dependent on the determined zone in which the machine operates.

A system and method of controlling an agricultural machine uses the following steps: (a) successively recording signals from at least one first sensor sensing at least one agronomic parameter of a field during an operation of the machine in the field, (b) successively recording signals from at least one second sensor sensing at least one operation parameter of the machine during the operation of the machine in the field, (c) spatially overlaying the signals of the first sensor and the second sensor, (d) determining from the overlaid values a respective zone in the field, and (e) controlling an actuator of the machine dependent on the determined zone in which the machine operates.