A method of predicting crop yield includes generating, via a processor, a plurality of vectors representative of growing conditions for a current time period and a plurality of vectors representative of growing conditions for a previous time period. The processor compares the plurality of vectors for the current time to the vectors of the previous time periods for corresponding growing conditions and determines which previous vectors are closest to the current vectors. The plurality of previous time periods are each associated with crop yields. Thus, the processor can determine a crop yield for the current time period for a selected crop producing field and crop type based on crop yields for the closest previous time periods.

Method and Apparatus for harvesting crops, the apparatus (1) comprising a carriage (2) provided with a harvesting device (41), a ground height measuring device to measure or estimate the ground height (S2) at each harvested crop, a crop height measuring device to measure the height of a crop(S4), a processor operatively connected to the ground height measuring device to generate baseline ground data (S3) and operatively connected to the crop height measuring device to determine a desired harvest height (S5), a comparator to compare the baseline ground data to the desired harvest height to determine if a particular crop is to be harvested by the harvesting device.

A computer-executable method relating to weeds, and a computer system. The method comprises: receiving an image (S11); recognizing one or more plants in the image in order to obtain the classification and/or names of the plants, and determining whether the plants are weeds (S12); and in response to determining that at least one plant is a weed, outputting information indicating that the at least one plant is a weed (S13).

The present invention discloses a method for selective crop management in real time. The method comprises steps of: (a) producing a biosensor plant, said biosensor plant comprises a visual biomarker, said biomarker is encoded by at least one modified genetic locus comprising (i) preselected reporter gene allele having a phenotype detectable by a sensor, and (ii) a regulatory region of a preselected gene allele responsive to at least one parameter or condition of said plant or its environment, said regulatory region is operably linked to said reporter gene, such that the expression of said reporter gene phenotype is correlated with the status of said at least one parameter or condition of said biosensor plant or its environment; (b) acquiring image data of a target area comprising a plurality of said biosensor plants via said sensor and processing said data to generate a signal indicative of the phenotypic expression of said reporter gene allele of said biosensor plant; and (c) communicating said signal to an execution unit communicably linked to the sensor, said execution unit is capable of exerting in real time a selective monitoring and/or treatment of said target area or a portion thereof comprising said biosensor plants, said treatment is being responsive to said status of said parameter or condition of the biosensor plant or its environment. The present invention further discloses systems and plants related to the aforementioned method.

A granular product detection system is provided. The system includes a first sensor or first sensor array configured to couple to an air cart or a fill system that fills a tank of the air cart with a granular product, wherein the first sensor or first sensor array is configured to automatically detect at least a product type of the granular product. The system also includes a controller coupled to the first sensor or first sensor array and configured to receive feedback from the first sensor or first sensor array to automatically determine a control parameter related to a conveyance of the granular product based at least on the product type of the granular product.

A farming machine moves through a field and performs one or more farming actions (e.g., treating one or more plants) in the field. Portions of the field may include moisture, such as puddles or mud patches. A control system associated with the farming machine may include a traversability model and/or a moisture model to help the farming machine operate in the field with the moisture. In particular, the control system may employ the traversability model to reduce the likelihood of the farming machine attempting to traverse an untraversable portion of the field, and the control system may employ the moisture model to reduce the likelihood of the farming machine performing an action that will damage a portion of the field.

Example embodiments provide systems and methods for simulating a disease outbreak using a relatively simple formula based on a limited number of input parameters. In particular, disease severity is computed based on a relationship between leaf wetness duration and average temperature during a wetness period. The resulting model is a physical, deterministic model that accepts hourly weather data as input and outputs the most significant severity event of disease infection during a specified (e.g., one-day) period. This information can then be used to guide the application of various treatments when they can be most effective (e.g., when predicted disease severity is at its worst).

A farming machine moves through a field and performs one or more farming actions (e.g., treating one or more plants) in the field. Portions of the field may include moisture, such as puddles or mud patches. A control system associated with the farming machine may include a traversability model and/or a moisture model to help the farming machine operate in the field with the moisture. In particular, the control system may employ the traversability model to reduce the likelihood of the farming machine attempting to traverse an untraversable portion of the field, and the control system may employ the moisture model to reduce the likelihood of the farming machine performing an action that will damage a portion of the field.

A system (100), method and computer program product for optimization of crop protection. A generator module (120) accesses one or more configuration data structures (220) wherein the one or more configuration data structures include data fields to store crop data (221), advice data (222) related to respective crop data, and crop protection product data (223) related to respective advice. Further, it accesses a plurality of code snippets (230) wherein each code snippet (231, 232, 233) has a condition which relates either to at least one property field of the one or more data structures (220) or to a result of another code snippet, and further includes generic program logic associated with the condition, and wherein each property field is used in the condition of at least one code snippet. The plurality of code snippets is applied to the one or more configuration data structures to generate an advice logic program.

Kits for vehicles may include pulse-width-modulated solenoids configured to selectably turn individual nozzle assemblies on and off and vary their flow rates when installed in fluid communication with the nozzle assemblies, one or more wirelessly-controllable solenoid controllers, a wiring harness to electrically connect the pulse-width-modulated solenoids to the controller(s), a wirelessly-communicating GPS antenna system, a LiDAR sensing system which may be wirelessly-communicating, associated wiring and bracketry to connect the kit with a vehicle, and a mobile device configured to wirelessly cause the one or more controllers to turn individual nozzle assemblies on and off and vary their flow rates based on sensed data and/or recorded data, in view of user-selected criteria.