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 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 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.