Tobacco is one of the major economical crops in the agriculture sector. It is essential to detect tobacco plants using unmanned aerial vehicle (UAV) images for improved crop yield and plays an important role in the early treatment of tobacco plants. The proposed research work is carried out in three phases: In the first phase, we collect images from UAV’s and apply the French Commision Internationale de l'eclairage (CIE) L*a*b colour space model as pre-processing operations and segmentation. And then two prominent motion descriptors namely histogram of flow (HOF) and motion boundary histogram (MBH) are combined with the optimal histogram of oriented gradients (HOG) descriptor for exploring optimal motion trajectory and spatial measurements. And finally, the spatial variations with respect to the scale and illumination changes are incorporated using the optimal HOG descriptor. Here both dense motion patterns and HOG are refined using hierarchical feature selection using principal component analysis (PCA). The proposed model is trained and evaluated on different tobacco UAV image datasets and done a comparative analysis of different machine learning (ML) algorithms. The proposed model achieves good performance with 95% accuracy and 92% of sensitivity.

<p>Provisioning smart intelligent transport for vehicular ad hoc networks (VANETs) depends on dissemination of safety-related messages. The performance of VANET are highly affected due vehicle density, mobility and environmental condition. Recently several research has been under development, the design of a rapid, flexible, efficient and reliable medium access control (MAC) which address the precise constraint of smart intelligent transport system in the highly dynamic VANET environment. Extensive survey carried out in this work shows TDMA (Time division medium access) based MAC approach outperform carrier sense medium access/ collision avoidance CSMA\CA based approach. However, TDMA based approach incurs bandwidth wastages. To utilize bandwidth more efficiently cognitive radio (CR) technique is adopted for designing efficient MAC. However, the existing CR model incurs computation overhead and is not evaluated under different environmental condition such as rural, highway and urban (RHU). To overcome research challenges, this work present efficient decentralized distributed MAC (DMAC) that minimize collision and maximize throughput. Experiment are conducted to evaluate the performance of DMAC over state-of-art model in terms of throughput, success transmission and collision achieved. The outcome shows significant performance over state-of-model.</p>

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