An agricultural system can include a first nozzle assembly positioned along a boom assembly and configured to selectively dispense an agricultural product therefrom. An airflow detection system can be configured to capture data indicative of one or more airflow sources. A computing system can be communicatively coupled to the first nozzle assembly and the airflow detection system. The computing system can be configured to receive, from the airflow detection system, the data associated with the one or more airflow sources and generate a first nozzle vector for the first nozzle assembly based at least in part on the data from the airflow detection system.

A system for controlling nozzle flow rate includes a master node having an expected overall flow rate module configured to generate an expected overall flow rate of an agricultural product based on one or more sprayer characteristics, and an adjustment module configured to generate an error correction based on a difference between the expected overall flow rate and an actual overall flow rate of the agricultural product. A plurality of smart nozzles are in communication with the master node, each of the smart nozzles includes an electronic control unit in communication with one or more control valves and one or more nozzle assemblies. Each of the smart nozzles includes a target smart nozzle flow rate module configured to generate a target smart nozzle flow rate of the agricultural product based on the one or more sprayer characteristics. The target smart nozzle flow rate is adjusted according to the error correction.

A farming machine moves through a field and includes an image sensor that captures an image of a plant in the field. A control system accesses the captured image and applies the image to a machine learned plant identification model. The plant identification model identifies pixels representing the plant and categorizes the plant into a plant group (e.g., plant species). The identified pixels are labeled as the plant group and a location of the pixels is determined. The control system actuates a treatment mechanism based on the identified plant group and location. Additionally, the images from the image sensor and the plant identification model may be used to generate a plant identification map. The plant identification map is a map of the field that indicates the locations of the plant groups identified by the plant identification model.

Described herein are implements and application units for placement of fluid applications with respect to agricultural plants of agricultural fields. In one embodiment, an application unit includes a frame to be positioned in operation between two rows of plants and a plurality of flexible members coupled to the frame in operation such that the plurality of flexible members guide a lateral position of the frame to be approximately equidistant from the two rows of plants based upon whether at least one flexible member of the plurality of flexible members contacts one or more plants of the two rows of plants. The plurality of flexible members include a plurality of fluid outlets for spraying crop input in close proximity to the rows of plants.

The invention discloses a traceability system for pesticide residues, the main points of the technical scheme: the traceability system for pesticide residues, comprising the following steps; step 1, collecting the basic properties of a variety of pesticides commonly used in crops; step 2, collecting the degradation properties of pesticides in many different types of soils; step 3, collecting the rainfall, irrigation conditions and other agricultural operation factors; step 4, predicting the risk of pesticide residues on the surface of the soil after use, and speculating on the transfer pollution of pesticides to crops; step 5, predicting the pesticide residues available to leach into groundwater after pesticide use; step 6, predicting the pesticide residues available to leach into surface water after pesticide use. The traceability system for pesticide residues provides a set of practical pesticide residues risk assessment tools for agricultural producers, environmental protection and legislative agencies, agricultural production management departments.

An agricultural implement includes the calibration of liquid fertilizer distribution for an agricultural implement. The calibration system measures the level of the liquid fertilizer or utilizes a known volume in a container and the pressure of the liquid fertilizer. The system uses the level and pressure to calculate the density of the liquid fertilizer, which helps achieve more consistent fertilizer application. Additionally, the density of the liquid fertilizer can be used to more accurately measure the tank level of a particular tank. The system can include a tilt sensor and valves so that when the agricultural implement is traversing a hill or otherwise rough terrain, the system can accurately measure tank level and selectively draw liquid fertilizer from a particular tank or tanks to mitigate spillage. The system also can measure and monitor the tank level of the tank or tanks and automatically fill the tank or tanks based on the measured tank level.

Systems and methods for monitoring plants'conditions in one or more plant growing areas are presented. The system comprises a data collection system for providing characterization data about various parameters of plants in the one or more plant growing areas, the data collection system comprising data collection modules of at least first and second different types comprising respectively one or more first type imaging devices of predetermined first field of view and first resolution and one or more second type imaging devices of predetermined second field of view narrower than the first field of view and second resolution higher than the first resolution, the characterization data provided by the first type imaging device(s) comprising first type image data indicative of one or more plants in the plant growing area and of location of at least one device of the second type imaging devices with respect to said one or more plants in the plant growing area, the characterization data provided by the second type imaging device(s) comprising second type image data indicative of one or more portions of plants in the plant growing area; and a control system for activating at least one first type imaging device and at least one second type imaging device at least partially simultaneously, and to be responsive to operational data being based on analysis of the first type image data and comprising navigation data to navigate the at least one second type imaging device or at least one device of the first type imaging devices in the plant growing area.

An automated farming system includes a frame. The frame includes a fixed base, a beam, and a support. A farming implement support extends from the beam and moves up and down in relation to the beam. The farming implement support moves along a length of the beam. The movable support includes a propulsion system and is configured to rotate around the fixed base. Movement of the farming implement support and the movable support allows for high density planting of crops in hexagonal patterns and/or a continuous spiral pattern.

The invention relates to weed mapping, monitoring and control, in particular to the mapping, monitoring and control of invasive annual grasses. A computer system comprises a receiving unit providing a processing unit with images of a geographical area, the images displaying the geographical area at points in time during a phenological cycle of a weed. At least two images depicting a weed at two different phenological stages may be used because two sequentially collected images are required in order to identify a characteristic temporal change. The processing unit analyzes the images to identify image areas showing a spectral signature characteristic of the weed. The processing unit identifies geographical sub-areas corresponding to the identified image areas and to create a weed distribution map with the identified geographical sub-areas marked as areas affected by the weed. The output unit is configured to output the map.

A robotic orchard spraying system having autonomous delivery vehicles (ADV), each autonomously delivering an amount of a premixed solution over a non-overlapping path verified by a forward-looking sensor, video, or both. Also, a mobile control center, configured to wirelessly inform the autonomous delivery vehicle of the path within the areas and to confirm that the autonomous delivery vehicle is following the path within the area. Additionally, a mapper vehicle generates the path within the area, the mapper vehicle being configured to communicate information about the path and the area to the command center. The mapper vehicle senses the path with a forward-looking LiDAR sensor, and senses the area with a GPS sensor. Moreover, a nurse truck has a reservoir of premixed solution for replenishing a tank of the ADV. ADVs and the control center communicate over a radio network, which may be a mesh network, a cellular network, or both.