Abstract: An example operation may include one or more of receiving, by an administrator node, a transaction request from a user node over a blockchain, activating, by the administrator node, a smart contract that corresponds to the transaction request, acquiring, by the administrator node, parameters from an oracle node based on the smart contract, modifying, by the administrator node, the smart contract based on the parameters, and executing, by the administrator node, the modified smart contract to commit the requested transaction to the blockchain.

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: An example operation may include one or more of receiving, by an administrator node, a transaction request from a user node over a blockchain, activating, by the administrator node, a smart contract that corresponds to the transaction request, acquiring, by the administrator node, parameters from an oracle node based on the smart contract, modifying, by the administrator node, the smart contract based on the parameters, and executing, by the administrator node, the modified smart contract to commit the requested transaction to the blockchain.

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: 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.