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 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.

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.

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.

The disclosure relates to a method for processing plants in a field in which a specific type of crop is planted, said method having the following steps: selecting a processing tool for processing plants; acquiring an image of the field, the image being correlated with position information; determining a position of a plant to be processed in the field using a neural network into which the acquired image is input, the neural network having a plurality of heads and in particular one of the heads being evaluated according to the processing tool and/or the type of crop grown; guiding the processing tool to the position of the plants; and processing the plants using the processing tool.

The present disclosure relates to a method for adjusting a working depth of a plough implement, the plough implement comprising a plurality of ground engaging tools for penetrating and moving soil and a depth adjustment apparatus configured to adjust a working depth of at least one of the ground engaging tools, wherein the method comprises receiving control-data indicative of at least one of an operation the plough implement or a field condition of a field across which the plough implement is moved; and automatically controlling an operation of the depth adjustment apparatus in a manner that adjusts a working depth of the at least one ground engaging tool on the basis of the control-data received.

A method and system provide the ability to manage an orchard. Sensor data that represents a first state of the orchard is captured via one or more sensors. The sensor data is captured as the one or more sensors are traveling through the orchard. An almanac is maintained. The almanac provides a state library of sequential states of a representative orchard and a task library for one or more tasks to be performed to transition between the sequential states. A task manager queries the almanac to identify a first task of the one or more tasks and allocates the first task to one or more robots that perform the first task.

A planting system includes a seed flow controller coupled to a hopper and a spreader. The seed flow controller, hopper, and spreader are transported by an unmanned aerial vehicle to spread the seeds over a geography. The seed flow controller includes several rollers having apposed outer surfaces. The rollers also include fins that extend about respective roller axes such that the fins cross at discrete contact points as the rollers rotate. The interfacing rollers break up clumps of seed material stored in the hopper and controllably convey the seed material from the hopper to the spreader. The spreader has a spinning spreader plate that flings the seed material laterally outward to spread the seed material over the ground.