A computer-implemented method of controlling a mobile agricultural machine includes receiving field map data representing a first agricultural operation performed on a field, receiving a location sensor signal indicative of a sensed geographic location of the mobile agricultural machine on the field, the mobile agricultural machine having a plurality of sections that are independently controllable to perform a second agricultural operation on the field that is different than the first agricultural operation, and generating a control signal to control the plurality of sections based on the field map data and the location sensor signal.

A system and method for automated lawn chemical treatment application are described. The system comprises a station having a series of concentrated chemicals capable of being mixed and diluted in a reservoir having a water supply to form a specialty formula and a craft which receives the specialty formula for dispersal. Various features of the craft enable a systematic treatment of various plants and/or areas of a law to reduce or eliminate the growth of unwanted plants while promoting the growth of desired plants. These may include the addition of imaging sensors and artificial intelligence to recognize unwanted plants versus desirable plants and develop means to inhibit or grow each over a specified period. A homeowner's ability to consistently treat plants with low-volumes of chemical treatments is enhanced, thus limiting environmental effects and reducing chemical waste.

An information map is obtained by an agricultural system. The information map maps values of a characteristic at different geographic locations in a worksite. An in-situ sensor detects tractive characteristic values as a mobile agricultural machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive tractive characteristic values at different geographic locations in the worksite based on a relationship between values of the characteristic in the information map and tractive characteristic values detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

An information map is obtained by an agricultural system. The information map maps values of a characteristic at different geographic locations in a worksite. An in-situ sensor detects material consumption values as a mobile material application machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive material consumption values at different geographic locations in the worksite based on a relationship between values of the characteristic in the information map and material consumption values detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

An information map is obtained by an agricultural system. The information map maps values of a characteristic at different geographic locations in a worksite. An in-situ sensor detects nutrient values as a mobile material application machine operates at the worksite. A predictive map generator generates a predictive map that maps predictive nutrient values at different geographic locations in the worksite based on a relationship between values of the characteristic in the information map and nutrient values detected by the in-situ sensor. The predictive map can be output and used in automated machine control.

Agricultural machines utilize global positioning systems (GPS) to acquire the location of the machine as well as the location of an event, which may be based upon an operation of the agricultural machine. Because of the possibility of outage and/or inaccuracy of the GPS, a GPS augmentation system can be included with the agricultural machine. The GPS augmentation system can supplement the location determination of the GPS, or can be used in place of the GPS when the GPS is not available. An unmanned vehicle can also be used as part of the augmentation system to provide additional information for the location of the agricultural machine and/or the event.

An agricultural seeder (10) for research plots includes a front gang (21) of planting units (22) and a rear gang (23) of planting units (24) spaced a distance behind the front gang. Metering systems (14) are provided for metering seed to the planting units (22, 24) of the front and rear gangs (21, 23). A tripping system (27) is used to start and stop the seed metering systems (14) and to change seed between plots while planting. A programmable logic controller (28) is programmed with the distance between the front and rear gangs (21, 23) and is used to delay the tripping of the metering system (26) for the rear gang (23) relative to the metering system (25) for the front gang (21) until the seeder (10) has traveled the distance (L) between the front and rear gangs (21, 23). The delayed tripping for the rear gang (23) keeps the planted rows and alleyways created by the front gang planting units (22) laterally aligned with those created by the rear gang planting units (24).

An agricultural product application system such as an air seeder or a sprayer includes a controller, a product source, and a plurality of product dispensing sections. Each of the dispensing sections include a metering module and a product sensor arranged to detect product passing through one or more product dispensing devices. The controller determines a dispense activation delay (d.sub.on) for each product dispensing section by measuring a delay between activation of the metering module and the product sensor detecting product arriving. The delay is optionally used for automatic control of the product dispensing sections and/or for logging georeferenced application data.

In an embodiment, yield data representing yields of crops that have been harvested from an agricultural field and field characteristics data representing characteristics of the agricultural field is received and used to determine a plurality of management zone delineation options. Each option, of the plurality of management zone delineation options, comprises zone layout data for an option. The plurality of management zone delineation options is determined by: determining a plurality of count values for a management class count; generating, for each count value, a management delineation option by clustering the yield data from and the field characteristics data, assigning zones to clusters, and including the zones in a management zone delineation option. One or more options from the plurality of management zone delineation options are selected and used to determine one or more planting plans. A graphical representation of the options and the planting plans is displayed for a user.

A computer-implemented method for providing at least one migration risk index, comprising the steps: providing soil data for a location, comprising at least information on the soil composition at that location (S10); providing weather data for said location, comprising at least historical precipitation data for said location (S20); providing a soil mobility model for a product, wherein the soil mobility model is configured to calculate migration data of the product at said location depending on the soil data and the weather data (S30); and determining at least one migration risk index based on the migration data and being representative of a risk that said product migrates down to a lower soil layer at said location (S40).