Abstract: A method for the control of phytopathogenic fungi in agricultural crops using a synergistic fungicidal composition comprising Zoxamide (A) and Cyflufenamid (B) on plants or on the soil in which said plants grow.

Abstract: An example method for communicating AI data from a sender device to a receiver device in a computing environment may include generating protocol instances of AI data; generating AI metadata description based on AI model information; sending a request comprising the AI metadata description to a receiver device for establishing a session; establishing a session by receiving a response from the receiver device based on the request; sending the AI data associated with the AI metadata description, to the receiver device based on the established session. The receiver device may receive the request and determine the compatibility of the metadata description, generate and send a response, followed by receiving the AI data associated with the AI metadata description.

Abstract: The present disclosure relates to methods and devices for managing communication of Artificial Intelligence (AI) data between devices in a computing environment. Detecting one or more sets of changes in data values associated with current data frame based on comparison between current data frame and previously generated data frame. Further, performing generation of one or more data chunks of an updated data frame, comprising one or more data segments based on the one or more sets of changes, based on one of, a first codec technique and second codec technique. Transmitting one or more data chunks to one or more receiver devices based on the respective data chunk satisfying a specified chunk size. Transmitting the one or more data chunks generated for updated data frame to one or more receiver devices based on respective data chunk satisfying a specified chunk size.

Abstract: A method and device for execution of deep neural network (DNN) in an internet of things (IoT) edge network are provided. In an embodiment, at least one edge device within communication range of an IoT device are selected. Further, a network for connecting the IoT device with the at least one selected edge device is identified. A split ratio is determined based on an inference time of the DNN and a transmission time required for transmitting output of each layer of DNN from the IoT device to the selected at least one edge device. Finally, a plurality of layers of the DNN are split into a first part and a second part based on the split ratio, and the second part is transmitted to the selected at least one edge device through the identified network. The first part is executed on the IoT device, and the second part is executed on the selected at least one edge device.

Abstract: A method and system of task management in an internet of things (IoT)-edge network may be provided. The method may include assigning at least one deep neural network (DNN) task from an IoT device to a first edge device; determining whether the first edge device satisfies one of a first predetermined criteria and a second predetermined criteria during execution of the at least one DNN task; triggering an alarm to the IoT device based on the determination; identifying a second edge device subsequent to the triggering of the alarm; determining whether to transfer the at least one DNN task to the second edge device or to execute the at least one DNN task on the first edge device, based on determining that the first device satisfies the second predetermined criteria; and transferring the at least one DNN task to the second edge device, based on determining that the first device satisfies the first predetermined criteria.

Abstract: A method and device for execution of deep neural network (DNN) in an internet of things (IoT) edge network are provided. In an embodiment, at least one edge device within communication range of an IoT device are selected. Further, a network for connecting the IoT device with the at least one selected edge device is identified. A split ratio is determined based on an inference time of the DNN and a transmission time required for transmitting output of each layer of DNN from the IoT device to the selected at least one edge device. Finally, a plurality of layers of the DNN are split into a first part and a second part based on the split ratio, and the second part is transmitted to the selected at least one edge device through the identified network. The first part is executed on the IoT device, and the second part is executed on the selected at least one edge device.