International Business Machines Corporation

Mukul Tewari

Jitendra Singh

Kuntal Dey

Lloyd Alan Treinish

Eli M Dow

Seema Nagar

Campbell D Watson

Saurav Basu

Sushain Pandit

Levente Klein

Vinayak Sastri

In a method for intelligently executing predictive simulator, a processor may input a previous input vector of conditions for a predictive simulator collected at a first time into a machine-learning (ML) model. A processor may input a current input vector of conditions for the predictive simulator collected at a second time into the ML model. A processor may determine using the ML model, a binary similarity index. The binary similarity index represents a prediction of similarity between a first output from the predictive simulator based on the previous input and a second output from the predictive simulator based on the current input.

Intelligent execution of compute intensive numerical simulation models

Method, apparatus, and computer program product are provided for estimating crop pest risk and/or crop disease risk at sub-farm level. In some embodiments, a farm region is determined based on farm definition data, and input data associated with the farm region are retrieved from a plurality of data sources. The input data may include a plurality of pixel sets. In some embodiments, for each pixel set, crop risk data are determined based on the input data using one or more spatiotemporal regression models. The crop risk data may include an estimate of crop pest risk and/or crop disease risk for each pixel set. In some embodiments, the farm region is categorized into a plurality of sub-farms each defining a risk level category for that sub-farm based on the crop risk data. In some embodiments, the sub-farms are displayed as a visual heat-map, along with recommended antidote options.

Estimation of crop pest risk and/or crop disease risk at sub-farm level

Road condition prediction for potentially hazardous road segments is described. For roadways that may contain accumulated frozen precipitation, a road segment for road condition prediction is selected based on weather conditions. Various models including a solar radiation budget model, a permanent structures model, a dynamic structures model, and a road condition model are generated for the selected road segment and account for shading effects on the road segment caused by objects near the road segment. A road condition prediction for hazardous conditions on the road segment is determined based on the road condition model and provided to a driver to alert the driver of any potentially hazardous conditions.

Road icing condition prediction for shaded road segments

Classifying land use by receiving geographic data and land use data for a geographic area, receiving surface temperature data for the geographic area, mapping the geographic data and temperature data to a set of map grid cells, determining temperature statistics for each map grid cell, training a machine learning model according to the land use data and temperature statistics, and classifying land use for map grid cells of a different geographic area according to the machine learning model.

Classifying land use using satellite temperature data

A computer-implemented method for effective agriculture and environment monitoring. The method may comprise measuring a desired variable over an area of interest using a remote inspection platform according to an inspection plan, predicting an occlusion of the remote inspection platform, and in response to the predicted occlusion, determining whether to invoke a local inspection platform to complete the inspection plan. The occlusion in some embodiments interrupts the inspection plan for the remote inspection platform.

Effective agriculture and environment monitoring

In an approach for forecasting environmental occlusion events, a processor receives a spatio-temporal zone of interest. A processor collects data associated with the spatio-temporal zone of interest. A processor builds a machine-learning model using the data. A processor generates an occlusion probability map for the spatio-temporal zone of interest based on the machine-learning model and enriched mathematical operators. A processor outputs the occlusion probability map.

Forecasting environmental occlusion events

Method, apparatus, and computer program product are provided for estimating crop type and/or sowing date. In some embodiments, a historical crop growth time series and a plurality of simulated crop growth time series are determined, and the historical time series is matched against each simulated time series to determine an estimated crop type and/or sowing date. For example, one simulated time series may be determined for each crop type/sowing date combination within a set of one or more crop types and one or more sowing dates based on historical crop data. Each time series represents crop growth in an area of interest and comprises element(s) including crop-specific parameter(s), such as leaf area index (LAI). The historical time series may be determined based on remote sensor data. Each simulated time series may be determined using a crop growth simulation model and based on historical crop data, geospatial data, and weather data.

Estimation of crop type and/or sowing date

A computer-implemented method can include obtaining irrigation data. The method can further include obtaining a first set of watering rates. The method can further include generating a first set and a second set of soil moisture estimates. The first and second sets of soil moisture estimates can be based at least in part on the irrigation data. The method can further include obtaining a custom constraint and making a first determination that the first set of soil moisture estimates satisfies the custom constraint. The method can further include obtaining a moisture reference value in response to making the first determination. The moisture reference value can be based at least in part on the second set of soil moisture estimates. The method can further include making a second determination that the moisture reference value exceeds a first threshold, and generating an irrigation plan in response to making the second determination.

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Lafayette, CO, United States of America

Mukul Tewari