A computer-implemented method of targeting grower fields for crop yield lift is disclosed. The method comprises receiving, by a processor, crop seeding rate data and corresponding crop yield data over a period of time regarding a group of fields associated with a plurality of grower devices; receiving, by the processor, a current seeding rate for a grower's field associated with one of a plurality of grower devices; determining, whether the grower's field will be responsive to increasing a crop seeding rate for the grower's field from the current seeding rate to a target seeding rate based on the crop seeding rate data and corresponding crop yield data; preparing, in response to determining that the grower's field will be responsive, a prescription including a new crop seeding rate and a specific hybrid to be implemented in the grower's field.

A system and method for crop management uses data of specific varieties, such as the effect of growing degree units (GDU) on the phenological stage and optimal soil moisture percentage (SMP) to predict crop growth, to water the crops, and to manage agricultural systems by suggesting planting dates required to meet harvest goals. For plants growing in irrigation tracts, the system and method may use soil moisture sensors and the phenological stage information to provide water to the plants. In other embodiments, predictions are made of harvest dates for planted varieties and/or planting dates to reach harvest goals. The effect of mulching may be taken into account.

A method of inspecting plants for contamination includes generating a first series of images of a plant using a camera mounted to a frame being moved along a planting bed by a harvester, identifying a region of interest displayed in the first series of images as a region of contamination on the plant based on a color criterion and a morphological criterion applied to the region of interest, and transmitting data including an instruction to increase a vertical distance between the plant and a cutter of the harvester to avoid harvesting the plant in response to identifying the region of interest as the region of contamination. The method further includes generating a second series of images of an additional plant as the frame continues to be moved along the planting bed by the harvester while the vertical distance between the plant and the cutter is being increased.

A soil measure, such as a soil cone index, and a vehicle index indicating the amount of force the vehicle exerts on the ground as it travels over the ground, are obtained and compared to identify a soil damage score. The soil damage score can be mapped over a field and an agricultural vehicle can be controlled based upon the soil damage score.

An integrated navigation method for a mobile vehicle is provided, which includes: acquiring a motion measurement of the mobile vehicle by using an inertial navigation element in the mobile vehicle and calculating a gesture parameter of the mobile vehicle based on the motion parameter; estimating, based on the gesture parameter, a motion state of the mobile vehicle in a real time manner by using a satellite navigation element in the mobile vehicle to obtain an error estimation value of the motion state, and correcting a motion parameter of the mobile vehicle based on the error estimation value of the motion state; and controlling an operation route of the mobile vehicle based on corrected navigation information.

Techniques are provided for generating target success group of hybrid seeds for target fields include a server receiving agricultural data records that represent crop seed data describing seed and yield properties of hybrid seeds and first field geo-location data for agricultural fields where the hybrid seeds were planted. The server receives second geo-locations data for target fields where hybrid seeds are to be planted. The server generates a dataset of hybrid seed properties that include yield values and environmental classifications for hybrid seeds and then a dataset of success probability scores that describe the probability of a successful yield on the target fields based on the dataset of hybrid seed properties and the second geo-location data. The server generates target success yield group of hybrid seeds and probability of success values based on success probability scores and a yield threshold. The server causes display of the target success yield group.

An agriculture support device includes a traveling creator to create a scheduled traveling route of an agricultural machine in an agricultural field, a display controller to display on an external terminal a virtual traveling status of the agricultural machine to travel on the scheduled traveling route created by the traveling creator, and a correction permitting controller to permit correction of the scheduled traveling route created by the traveling creator when the external terminal requests the correction. The display controller displays, on the external terminal, the virtual traveling status and a result traveling status of the agricultural machine that has traveled on the scheduled traveling route.

A soil measure, such as a soil cone index, and a vehicle index indicating the amount of force the vehicle exerts on the ground as it travels over the ground, are obtained and compared to identify a soil damage score. The soil damage score can be mapped over a field and an agricultural vehicle can be controlled based upon the soil damage score. In another example, a detector detects a fill level of a material storage compartment on an agricultural vehicle. The inflation pressure of tires on the agricultural vehicle is controlled based upon the detected fill level.

An example computer-implemented method includes receiving a plurality of agricultural data records including yield properties of products grown in fields and raw field features of the fields. The method also includes transforming the raw field features into distinct feature classes that characterize key features affecting yield of the one or more products, and generating, using data from the plurality of agricultural data records and the distinct feature classes, genomic-by-environmental relationships between one or more products, yield properties of the one or more products, and field features associated with the one or more products. Further, the method includes generating, based at least in part on the genomic-by-environmental relationships, predicted yield performance for a set of products associated with one or more target environments, generating product recommendations for the one or more target environments based on the predicted yield performance for the set of products, and providing one or more instructions configured to cause display of the product recommendations.

A system for monitoring soil composition within a field may have a ground-engaging tool configured to engage soil within a field as an implement moves across the field. The system may further have a sensor configured to generate data indicative of a soil composition within the field, where the sensor is movable relative to the ground-engaging tool while the implement moves across the field such that the sensor generates data indicative of the soil composition at different depths within the field. Additionally, the system may have a controller communicatively coupled to the sensor, with the controller being configured to determine the soil composition at the different depths within the field based at least in part on the data received from the sensor.