Implementations are described herein for normalizing counts of plant-parts-of-interest detected in digital imagery to account for differences in spatial dimensions of plants, particularly plant heights. In various implementations, one or more digital images depicting a top of a first plant may be processed. The one or more digital images may have been acquired by a vision sensor carried over top of the first plant by a ground-based vehicle. Based on the processing: a distance of the vision sensor to the first plant may be estimated, and a count of visible plant-parts-of-interest that were captured within a field of view of the vision sensor may be determined. Based on the estimated distance, the count of visible plant-parts-of-interest may be normalized with another count of visible plant-parts-of-interest determined from one or more digital images capturing a second plant.

A computing system includes a processor and a non-transitory, computer-readable medium including instructions that, when executed by the processor, causes the computing system to receive a machine data set; process the machine data set using a trained machine-learned model to generate predicted variety profile index values, and transmit the variety profile index values to a client computing device. A computer-implemented method includes receiving a machine data set; processing the machine data set using a trained machine-learned model to generate predicted variety profile index values, and transmitting the variety profile index values to a client computing device. A non-transitory computer-readable medium includes instructions stored thereon that, when executed by one or more processors, cause a computer to receive a machine data set; process the machine data set using a trained machine-learned model to generate predicted variety profile index values, and transmit the variety profile index values to a client computing device.

Systems, methods, and apparatuses for calibrating a first meter of an air cart include determining a calibration factor using a second meter. The second meter may be located with the air cart or remotely from the air cart. An amount of material dispensed from the second meter is used to determine the calibration factor. A number of cycles of operation of the second meter may also be used to determine the calibration factor. The calibration factor may be provided via a wired or wireless connection to a controller operable to control operation of the first meter.

The work vehicle includes a vehicle body, a cabin mounted on the vehicle body and having a roof, and a positioning device located above the cabin and configured to detect a position of the vehicle body on the basis of a signal transmitted from a positioning satellite. The roof has a roof front end located in a forefront portion in a front-rear direction; and a roof uppermost end located behind the roof front end and located higher than the roof front end. A position of the positioning device is lower than the roof uppermost end.

A system includes one or more processors; and one or more non-transitory, computer-readable media including instructions that, when executed by the one or more processors, cause the computing system to: receive a machine data set; process the machine data set with a trained machine-learned model to generate predicted variety profile index values; and cause a visualization to be displayed. A computer-implemented method includes receiving a machine data set; processing the machine data set with a trained machine-learned model to generate predicted variety profile index values; and causing a visualization to be displayed. A non-transitory computer-readable medium includes computer-executable instructions that, when executed by one or more processors, cause a computer to: receive a machine data set; process the machine data set with a trained machine-learned model to generate predicted variety profile index values; and cause a visualization to be displayed.

Described are various embodiments of a crop phenology characterisation method, and system using same. One embodiment relates to a method of monitoring a crop phenology, the method comprising acquiring a size measurement of a crop in a crop location over time, monitoring a first parameter calculated based at least in part on to a periodic recovery value of the size measurement, and a second parameter calculated at least in part based on a periodic growth value of the size measurement. In some embodiments, one or more of the first parameter and the second parameter are indicative of a crop characteristic. In some embodiments, an indication related to the crop characteristic may be provided in response to one or more of the first parameter and the second parameter.

A yield estimation system comprising a harvester. The harvester comprising a plurality of row units, one or more stalk sensors on each of the plurality of row units, and a yield monitor in communication with the plurality of row units. The system also comprising a processor configured to correlate data from the one or more stalk sensors to data from the yield monitor on a row-by-row basis and a display configured to display the correlated data to a user.

A working vehicle includes a vehicle body to be coupled to a working device, a communication controller to obtain first operation information relating to operation of the working device, the communication controller being electrically connected to the working device to communicate bi-directionally with the working device, an applicability judging device to judge applicability of the working device to the operation based on the first operation information obtained by the communication controller, and a display device to display the applicability judged by the applicability judging part.

A computer-implemented data processing method providing an improvement in executing machine learning processes on digital data representing physical properties related to agriculture is described. In an embodiment, the method comprises: receiving, from a computing device, a request to browse machine learning models stored in a digital model repository; retrieving, from the digital model repository and transmitting to the computing device, information about the machine learning models stored in the digital model repository; receiving, from the computing device, a selection, from the machine learning models, of a particular model and receiving particular input for the particular model; using resources available in a model execution infrastructure platform, executing the particular model on the particular input to generate particular outputs; transmitting the particular output to a computer configured on an agricultural machine to control the agricultural machine as the agricultural machine performs agricultural tasks in an agricultural field.

In an embodiment, an agricultural intelligence computing system stores a digital model of crop growth, the digital model of crop growth being configured to compute nutrient requirements in soil to produce particular yield values based, at least in part, on data unique to an agricultural field. The system receives agronomic field data for a particular agronomic field, the agronomic field data comprising one or more input parameters for each of a plurality of locations on the agronomic field, nutrient application values for each of the plurality of locations, and measured yield values for each of the plurality of locations. The system computes, for each location of the plurality of locations, a required nutrient value indicating a required amount of nutrient to produce the measured yield values. The system identifies a subset of the plurality of locations where the computed required nutrient value is greater than the nutrient application value. The system computes, for each of the subset of the plurality of locations, a residual value comprising a difference between the required nutrient value and the nutrient application value. The system generates a residual map comprising the residual values at the subset of the plurality of locations. Using the residual map and the one or more input parameters for each of the plurality of locations, the system generates and stores particular model correction data for the particular agronomic field.