Abstract: A system and method detect a change within an agricultural field during a growing season. The system and method involve the computation of correlation coefficients between pairs of images in a time series of images over an area, for example an agricultural field. Through comparisons of correlation coefficients over time, sudden field changes can be detected. When these field changes meet specified criteria (such as a minimum value threshold), a notification (alert) can be sent to an agricultural grower, informing them of the change in crop condition, and allowing them to view the changes in the field.

Abstract: An apparatus is provided for use with an original equipment manufacturer (OEM) scale associated with a grain cart. The apparatus includes a real-time telemetry weight device operatively connected with the OEM scale. The apparatus is configured to allow for a parallel mode of operation which provides for simultaneous operation of the real-time telemetry weight device and the OEM scale without interference while using a same set of load cells.

Abstract: A method and system for crop health change monitoring is provided. The method includes acquiring a companion image of a crop growing within a field at a first point in time, acquiring a master image of the crop growing within the field at a second point in time, and computing, using a processor, vegetation indices using the master image and the companion image for subsequently determining regions of change within the master image. An alert condition is determined if change within one or more of the regions of change is sufficient to meet defined criteria. If a minimum field area threshold is met, a modulation coefficient is applied to the defined criteria that determines the alert condition. The coefficient reduces area-based thresholds used in health detection in a manner proportional to the size of the agricultural field of interest.

Abstract: Methods and systems used for the classification of a crop grown within an agricultural field using remotely-sensed image data. In one example, the method involves unsupervised pixel clustering, which includes gathering pixel values and assigning them to clusters to produce a pixel distribution signal. The pixel distribution signals of the remotely-sensed image data over the growing season are summed up to generate a temporal representation of a management zone. Location information of the management zone is added to the temporal data and ingested into a Recurrent Neural Network (RNN). The output of the model is a prediction of the crop type grown in the management zone over the growing season. Furthermore, a notification can be sent to an agricultural grower or to third parties/stakeholders associated with the grower and/or the field, informing them of the crop classification prediction.

Abstract: A method for determining management zones within an agricultural field, the method includes selecting a plurality of remotely sensed images of the agricultural field wherein the plurality of remotely sensed images represent a plurality of growing seasons, each of the plurality of remotely sensed images having a vegetation index associated therewith, generating a refined average image from the plurality of remotely sensed images of the agricultural field, and applying a classification method to define management zones associated with the refined average image.

Abstract: A yield model generates a yield map for an agricultural field. A measurement system generates measured indicators that are a measurement or quantification of crop yield in the agricultural field. An observation system generates observed indicators that are spatial agricultural datasets describing observed characteristics of the agricultural field. To generate the yield map, the yield model generates a field array representing the agricultural field. The yield model generates an input array and a yield array by mapping the observed indicators and measured indicators to cells of the field array, respectively. The yield model determines a yield value for each cell of the yield array not including a mapped indicator using information included in the corresponding cells of the input array. The yield model generates a yield map using the determined yield values and the yield values in the yield array.

Abstract: A method for predicting crop yield of an agricultural field may include steps of applying a pre-season model to provide a pre-season model crop yield prediction, applying an in-season model to provide an in-season model crop yield prediction, applying a statistical imagery model to provide a statistical imagery model crop yield prediction, applying a histogram-based image model to provide a histogram-based image model crop yield prediction, applying crop-specific models to provide at least one crop-specific model crop yield prediction, and combining at a computing system crop yield predictions from a plurality of models within a set comprising the pre-season model, the in-season model, the statistical imagery model, the histogram-based image model, and the crop-specific models, to produce a final crop yield prediction.

Abstract: A telematics device associated with an agricultural vehicle for communicating and collecting agricultural data is provided. The telematics device includes a telematic device housing, a processor disposed within the telematic device housing, the processor coupled to a wireless transceiver for providing wireless connectivity for communicating the agricultural data and an interface coupled to the processor for receiving the agricultural data from at least one of the agricultural vehicle and an implement towed by the agricultural vehicle. The processor is configured to determine a measure of the wireless connectivity associated with the wireless transceiver and adjust a data transfer rate up or down based on the measure of the wireless connectivity.

Abstract: A method for determining harvest state of an agricultural field includes obtaining an observed image of the agricultural field from a data store containing agricultural image data, filtering the observed image using an image filtering module to provide a filtered image for processing, and assigning coordinates to the filtered image, the assigning coordinates to the filtered image performed by a coordinate projection module to provide an input image, processing the input image to determine a set of image statistics for the input image using a statistics calculation module, determining by a harvest state prediction module the harvest state of the agricultural field using the set of image statistics, wherein the harvest state is selected from a set including a pre-harvest state, an in-harvest state, and a post-harvest state and electronically transmitting a notification of the harvest state of the agricultural field to a computing device.

Abstract: A method and system for crop health change monitoring is provided. The method includes acquiring a companion image of a crop growing within a field at a first point in time, acquiring a master image of the crop growing within the field at a second point in time, and computing, using a processor, vegetation indices using the master image and the companion image, determining, using the processor, regions of change within the master image using the vegetation indices and generating an alert indicative of a change in crop condition of the crop growing within the field, and communicating the alert indicative of the change in crop condition over a network to a computing device configured to receive the alert.

Abstract: A weather prediction model predicts a weather state for a query location within a region. The weather prediction model accesses current and/or historical indicator weather states from weather measurement systems located near the query location. The weather measurement systems that are located near a query location are those within a proximity distance of the query location. The weather prediction model partitions the region into sectors each containing a number of the weather measurement systems. The weather prediction model determines a representative weather state for each sector at a representative distance from the query location using the indicator weather states. The weather prediction model predicts a weather state at the query location by interpolating the representative weather states for the sectors. The weather prediction model transmits the predicted weather state to a user responsible for agricultural management of the query location.

Abstract: A method for predicting crop yield of an agricultural field may include steps of applying a pre-season model to provide a pre-season model crop yield prediction, applying an in-season model to provide an in-season model crop yield prediction, applying a statistical imagery model to provide a statistical imagery model crop yield prediction, applying a histogram-based image model to provide a histogram-based image model crop yield prediction, applying crop-specific models to provide at least one crop-specific model crop yield prediction, and combining at a computing system crop yield predictions from a plurality of models within a set comprising the pre-season model, the in-season model, the statistical imagery model, the histogram-based image model, and the crop-specific models, to produce a final crop yield prediction.

Abstract: A method for determining management zones within an agricultural field, the method includes selecting a plurality of remotely sensed images of the agricultural field wherein the plurality of remotely sensed images represent a plurality of growing seasons, each of the plurality of remotely sensed images having a vegetation index associated therewith, generating a refined average image from the plurality of remotely sensed images of the agricultural field, and applying a classification method to define management zones associated with the refined average image.

Abstract: A telematics device associated with an agricultural vehicle for communicating and collecting agricultural data is provided. The telematics device includes a telematic device housing, a processor disposed within the telematic device housing, the processor coupled to a wireless transceiver for providing wireless connectivity for communicating the agricultural data and an interface coupled to the processor for receiving the agricultural data from at least one of the agricultural vehicle and an implement towed by the agricultural vehicle. The processor is configured to determine a measure of the wireless connectivity associated with the wireless transceiver and adjust a data transfer rate up or down based on the measure of the wireless connectivity.

Abstract: A method for shadow and cloud masking for remote sensing images of an agricultural field using a convolutional neural network, the method includes electronically receiving an observed image, the observed image comprising a plurality of pixels and each of the pixels associated with corresponding band information and determining by a cloud mask generation module executing on the at least one processor a classification for each of the plurality of pixels in the observed image using the band information by applying a classification model, the classification model comprising a convolutional neural network comprising a plurality of layers of nodes. The cloud mask generation module applies a plurality of transformations to transform data between layers in the convolutional neural network to generate a cloud map.

Abstract: A method for determining harvest state of an agricultural field includes obtaining an observed image of the agricultural field from a data store containing agricultural image data, filtering the observed image using an image filtering module to provide a filtered image for processing, and assigning coordinates to the filtered image, the assigning coordinates to the filtered image performed by a coordinate projection module to provide an input image, processing the input image to determine a set of image statistics for the input image using a statistics calculation module, determining by a harvest state prediction module the harvest state of the agricultural field using the set of image statistics, wherein the harvest state is selected from a set including a pre-harvest state, an in-harvest state, and a post-harvest state and electronically transmitting a notification of the harvest state of the agricultural field to a computing device.

Abstract: A method for shadow and cloud masking for remote sensing images of an agricultural field using multi-layer perceptrons includes electronically receiving an observed image, performing using at least one processor an image segmentation of the observed image to divide the observed image into a plurality of image segments or superpixels, extracting features for each of the image segments using the at least one processor, and determining by a cloud mask generation module executing on the at least one processor a classification for each of the image segments using the features extracted for each of the image segments, wherein the cloud mask generation module applies a classification model including an ensemble of multilayer perceptrons to generate a cloud mask for the observed image such that each pixel within the observed image has a corresponding classification.

Abstract: A method for effective nitrogen application may include acquiring imagery of an agricultural field and delineating a plurality of management zones within the agricultural field using the imagery of the agricultural field. For each of the management zones within the agricultural field, the method may include receiving soil characteristics at a computing device, the soil characteristics derived from physical soil samples within the management zones. For each of the management zones within the agricultural field, the method may include receiving weather data at the computing device, management practice information and crop cultivar identification at the computing device. The model simulates effects of in-season nitrogen applications on crop yields within each of the management zones.

Abstract: A method and system for crop health change monitoring is provided. The method includes acquiring a companion image of a crop growing within a field at a first point in time, acquiring a master image of the crop growing within the field at a second point in time, and computing, using a processor, vegetation indices using the master image and the companion image, determining, using the processor, regions of change within the master image using the vegetation indices and generating an alert indicative of a change in crop condition of the crop growing within the field, and communicating the alert indicative of the change in crop condition over a network to a computing device configured to receive the alert.

Abstract: Embodiments relate to calibrating yield data points. A calibration system receives a plurality of groups of yield data points. Each group is associated with an attribute, such as, a machine identifier, a zone identifier, a localized zone identifier, and/or a moisture identifier. The system calculates a grand aggregate yield based on the yield data points of the plurality of groups. The system calculates a group aggregate yield based on yield data points of each group. The system subtracts the group aggregate yield from each yield data point of the group producing adjusted yield data points. The system adds the grand aggregate yield to each of the adjusted yield data points producing calibrated yield data points.