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 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 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 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: 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 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 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 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.