Abstract: In an agricultural field having first regions with current soil test values known from actual tests and second regions having unknown soil test values, nutrient levels are predicted using a soil test model which defines a statistical relationship between (i) nutrient levels for a given region of a training field for a given season and (ii) field specific characteristics for the given region in a previous growing season and nutrient levels in the given region or proximate regions from the given growing season. Acquired known field specific characteristics and current soil test values from the first regions are then applied to the soil test model to calculate the predicted nutrient level the second regions. This can reduce the cost of soil sampling by using actual soil test results from one management zone as a predictor when modeling other zones' properties.

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 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.