Described herein are implements and applicators for placement of fluid applications with respect to agricultural plants of agricultural fields. In one embodiment, a fluid applicator includes at least one applicator arm, a first nozzle disposed to apply fluid to a rhizosphere of the plants, and a second nozzle disposed to apply fluid between the row of plants.

The present invention relates to the autonomous application of crop protection products by means of a drone. The present invention relates to a process and to an unmanned aerial vehicle for applying crop protection product taking into consideration drift phenomena. The present invention furthermore relates to a computer program product which can be employed for controlling the process according to the invention.

Described herein are methods and systems for generating instructions for variable rate application of crop protection materials, comprising computing a total dosage of crop protection material(s) allowed for application in a crop field such that crop protection material(s) residue in products of crop planted in the crop field does not exceed a predefined MRL, adjusting the total dosage according to residue affecting parameters, mapping the crop field to segments according to pest distribution of one or more pests in each segment, computing a dosage of the crop protection material(s) estimated to effectively control the pest(s) in each segment based on the pest distribution in the respective segment, computing a variable rate application of the crop protection material total dosage in the crop field based on the dosage estimated for each segment, and outputting instructions to apply the crop protection material(s) in the crop field according to the variable rate application.

The invention relates to a method for cultivating row crops (200), comprising the following steps: sensor and satellite based recording of locations and/or courses of rows of plants (202a-202l ) while a first cultivation measure is carried out on a row crop (200) by means of an agricultural plant cultivation device (10) and controlling the movement and/or operation of an agricultural plant cultivation device (10) while a second cultivation measure is carried out on the row crop (200) based on the recorded locations and/or courses of the rows of plants (202a-2021).

Providing an object detection engine, training the object detection engine to identify a weed, training the object detection engine to identify a crop, providing an image from a sensor to the object detection engine, discerning with the object detection engine the weed from the crop, and plotting a path from the weed to a spot spray assembly upon identification of the weed by the object detection engine.

An agricultural method includes providing a positive air pressure chamber to prevent outside contaminants from entering the chamber; growing crops in a plurality of cells in the chamber, each cell having multi-grow benches or levels, each cell further having connectors to vertical hoists for vertical movements in the chamber; maintaining pre-set temperature, humidity, carbon dioxide, watering and lighting levels to achieve predetermined plant growth; using motorized transport rails to deliver benches for operations including seeding, harvesting, grow media recovery, and bench wash; dispensing seeds in the cell with a mechanical seeder coupled to the transport rails; growing the crops with computer controlled nutrients, light and air level; and harvesting the crops and delivering the harvested crop at a selected outlet of the chamber.

Methods and systems for controlling robotic actions for agricultural tasks are disclosed which use a spacing-aware plant detection model. A disclosed method, in which all steps are computer-implemented, includes receiving, using an imager moving along a crop row, at least one image of at least a portion of the crop row. The method also includes using the at least one image, a plant detection model, and an average inter-crop spacing for the crop row to generate an output from the plant detection model. The plant detection model is spacing aware in that the output of the plant detection model is altered or overridden based on the average inter-crop spacing. The method also includes outputting a control signal for the robotic action based on the output from the biased plant detection model. The method also includes conducting the robotic action for the agricultural task in response to the control signal.

The present invention relates to the control of harmful organisms which may appear during the cultivation of crop plants. The control is effected on the basis of the prediction of subarea-specific infestation risks.

A kind of real-time measurement system and method for height from spray boom to crop canopy was provided in present invention patent, which includes the following steps. Step 1: Calibration for ultrasonic ranging sensor; Step 2: Determination of included angle for two ultrasonic ranging sensors; Step 3: Collection of relative height between spray boom and crop canopy; Step 4: Anti-interference processing for height data; Step 5: Calculation of relative height from spray boom to crop canopy. Its characteristic lies in: two ultrasonic ranging sensors with a certain angle are installed at each end of the spray boom, the height of crop canopy was acquired under the control of control unit, and the relative height from spray boom to crop canopy was obtained by the anti-interference analysis and data conversion, the accurate monitoring for relative height from spray boom to crop canopy was realized while the spray boom is tilted. It breaks through the technical bottleneck that the relative height of the spray boom and the crop canopy cannot be accurately obtained when the spray boom tilts due to turbulence in the field operation of the spray boom sprayer, and lays a technical foundation for the dynamic adjustment of the balance and height of the spray boom.

A computing system includes image receiving logic configured to receive image data indicative of an image of a field, ground identification logic configured to identify a first image portion of the image representing ground in the field, image segmentation logic configured to identify a remaining image portion that omits the first image portion from the image, and crop classification logic configured to apply a crop classifier to the remaining image portion and identify a second image portion of the image that represents locations of crop plants in the field. The computing system also includes weed identification logic configured to identify locations of weed plants in the field based on the identification of the first and second image portions and control signal generation logic configured to generate a machine control signal based on the identified locations of the weed plants.