Accurate and timely detection of wheat diseases remains crucial for sustainable agriculture, particularly in major wheat-producing regions. Wheat diseases pose a significant threat to global food security, need precise and timely detection to promote sustainable agriculture. Existing approaches consistently employ single-scale features with shallow-layered convolutional neural networks (CNNs). To bridge the research gaps, we introduce a novel Multi-Scale Wheat Disease Network (MSWDNet) with feature collaboration for wheat disease recognition supported by a comprehensive dataset collected from wheat fields. This study fills research gaps by introducing a novel technique to improve detection a... More >

This paper proposes designing and structuring a Cyber-Physical System (CPS) with a specific focus on vehicles equipped with on-board diagnosis (OBD-II). The purpose of the CPS is to collect and assess data pertaining to the vehicle's Electronic Control Unit (ECU), such as engine RPM, speed, and other relevant parameters. The OBD-II scanner utilizes the obtained data on mass airflow (MAF) and vehicle speed to compute CO2 gas emissions and fuel consumption. The data is wirelessly communicated using a GSM module to a Semantic Web. The CPS also uses GPS tracking to ascertain the vehicle's whereabouts. A Semantic Web is utilized to construct a database management system that stores and manages se... More >