Distributed and Analogous simulation framework for the control of pests and diseases in plants using IoT Technology

Abstract

In contemporary society, agriculture is progressively embracing technological innovations called Precision Agriculture. The utilization of various pest control and disease management strategies is of considerable importance in the surveillance of plants. The current framework encounters multiple challenges. The pest control and disease surveillance system employs a solitary Graphical Processing Unit (GPU) to manage the diverse array of connected sensors. Hence, this paper proposes utilizing the Distributed and Analogous Simulation Framework (DASF) in conjunction with the Internet of Things (IoT) to address the issue of pest control and diseases in plants. The approach reduces the strain on a specific GPU, effectively allocates the computational tasks across all accessible GPUs concurrently, and ensures continuous data transmission to the dashboards even in the event of GPU malfunction. The implementation of this procedure is anticipated to result in a reduction in overall system performance. In the DASF multi-threading framework, the allocation of tasks to particular auxiliary cores is performed by each GPU unit. The execution of the different functions within this system is allocated among four levels: disease management, pest recognition and control, output operations, and input functions. The data is analyzed concurrently and managed in a proficient and regulated manner. The proposed system demonstrates a significant enhancement in performance measures, with a value of 99.05%.