Abstract: Material waste screening is provided. A sensor obtains data related to an object. A processor classifies the object based on the data to identify a recycle category for the object, open a recycle bin for the identified recycle category, instruct the operator to deposit the object in the opened recycle bin, determine a level of compliance of the object, and create at least one new instruction to increase the level of compliance.

Abstract: Systems and methods are described for supply chain vulnerability forecasting using external event data is described. The method may include identifying historic data from each entity of a plurality of entities connected in a supply chain, aggregating temporal-based information relating to the entities and to relations among the entities into one or more time periods based on the historic data, training a cognitive prediction model using the aggregated temporal-based information, generating a vulnerability score for each entity, generating a recommendation for at least one entity if the vulnerability score for the at least one entity is above a preset threshold, retraining the cognitive prediction model using changed data of the at least one entity, and adjusting the vulnerability score based on the retraining.

Abstract: An evapotranspiration mitigation method, system, and computer program product include checking a condition via a first sensor connected to an outside of a device to compute an evapotranspiration level and activating a roof opening of the device to deploy a roof from the device if the evapotranspiration level is greater than a pre-determined threshold.

Abstract: A method for improving the performance of a computer vision system includes obtaining input specifying a task to be performed by the computer vision system; obtaining a first digital image; and comparing the first digital image to at least one training image used to train the computer vision system to solve the task. Further steps include, based on the comparing indicating that the first digital image is insufficiently similar to the at least one training image, recommending at least one adjustment to the digital image; obtaining a second digital image in accordance with the adjustment; and performing the task with the computer vision system based on the second digital image obtained in accordance with the adjustment. Adjustments can be based, for example, on image composition and/or weather conditions.

Abstract: Attributes associated with a farming area and production data may be received. A user's expertise level may be identified. At least one crop model may be executed to generate a plurality of scenarios specifying specific actions to perform associated with a farming topic. A production goal associated with the farming area may be received. Based on the production goal and the user expertise level, the plurality of farming scenarios may be filtered to generate at least one candidate scenario. A candidate scenario may be presented for execution. An app such as a chat bot may perform a dialog with the user in executing steps of the candidate scenario.

Abstract: In a precision agriculture application, using an imagery task constraint corresponding to an image capture task, an autonomous vehicle (AV) is selected to perform the task. The AV is caused to perform the task according to the imagery task constraint, causing the AV to autonomously record image data of an area in a field of view of the AV. Image data responsive to the task is received from the AV. From analysis of the image data using a processor and a memory, a material distribution task and a corresponding distribution task constraint are generated. Using the distribution task constraint, a second AV to perform the material application task is selected. The second AV is caused to perform the material distribution task according to the distribution task constraint, causing the autonomous vehicle to autonomously trigger dispersal of a material in an area in a field of view of the autonomous vehicle.

Abstract: An evapotranspiration mitigation method, system, and computer program product include checking a condition via a first sensor connected to an outside of a device to compute an evapotranspiration level and activating a roof opening of the device to deploy a roof from the device if the evapotranspiration level is greater than a pre-determined threshold.

Abstract: Material waste screening is provided. A sensor obtains data related to an object. A processor classifies the object based on the data to identify a recycle category for the object, open a recycle bin for the identified recycle category, instruct the operator to deposit the object in the opened recycle bin, determine a level of compliance of the object, and create at least one new instruction to increase the level of compliance.

Abstract: Systems and methods are described for supply chain vulnerability forecasting using external event data is described. The method may include identifying historic data from each entity of a plurality of entities connected in a supply chain, aggregating temporal-based information relating to the entities and to relations among the entities into one or more time periods based on the historic data, training a cognitive prediction model using the aggregated temporal-based information, generating a vulnerability score for each entity, generating a recommendation for at least one entity if the vulnerability score for the at least one entity is above a preset threshold, retraining the cognitive prediction model using changed data of the at least one entity, and adjusting the vulnerability score based on the retraining.

Abstract: A method for improving the performance of a computer vision system includes obtaining input specifying a task to be performed by the computer vision system; obtaining a first digital image; and comparing the first digital image to at least one training image used to train the computer vision system to solve the task. Further steps include, based on the comparing indicating that the first digital image is insufficiently similar to the at least one training image, recommending at least one adjustment to the digital image; obtaining a second digital image in accordance with the adjustment; and performing the task with the computer vision system based on the second digital image obtained in accordance with the adjustment. Adjustments can be based, for example, on image composition and/or weather conditions.

Abstract: Attributes associated with a farming area and production data may be received. A user's expertise level may be identified. At least one crop model may be executed to generate a plurality of scenarios specifying specific actions to perform associated with a farming topic. A production goal associated with the farming area may be received. Based on the production goal and the user expertise level, the plurality of farming scenarios may be filtered to generate at least one candidate scenario. A candidate scenario may be presented for execution. An app such as a chat bot may perform a dialog with the user in executing steps of the candidate scenario.

Abstract: One example of a computer-implemented method for adaptively placing weather sensors in response to dynamic local conditions includes obtaining a set of data indicating a dynamic local condition in a geographic location of interest and adaptively modifying a placement of a plurality of weather sensors in the geographic location of interest in response to the dynamic local condition.

Abstract: One example of a computer-implemented method for adaptively placing weather sensors in response to dynamic local conditions includes obtaining a set of data indicating a dynamic local condition in a geographic location of interest and adaptively modifying a placement of a plurality of weather sensors in the geographic location of interest in response to the dynamic local condition.

Abstract: One example of a computer-implemented method for adaptively placing weather sensors in response to dynamic local conditions includes obtaining a set of data indicating a dynamic local condition in a geographic location of interest and adaptively modifying a placement of a plurality of weather sensors in the geographic location of interest in response to the dynamic local condition.

Abstract: One example of a computer-implemented method for adaptively placing weather sensors in response to dynamic local conditions includes obtaining a set of data indicating a dynamic local condition in a geographic location of interest and adaptively modifying a placement of a plurality of weather sensors in the geographic location of interest in response to the dynamic local condition.