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Smart cities can be complemented by fusing various components and incorporating recent emerging technologies. IoT communications are crucial to smart city operations, which are designed to support the concept of a “Smart City” by utilising the most cutting-edge communication technologies to enhance city administration and resident services. Smart cities have been outfitted with numerous IoT-based gadgets; the Internet of Things is a modular method to integrate various sensors with all ICT technologies. This paper provides an overview of smart cities’ concepts, characteristics, and applications. We thoroughly investigate smart city applications, challenges, and possibilities with solutions in recent technological trends and perspectives, such as machine learning and blockchain. We discuss cloud and fog IoT ecosystems in the in capacity of IoT devices, architectures, and machine learning approaches. In addition we integrate security and privacy aspects, including blockchain applications, towards more trustworthy and resilient smart cities. We also highlight the concepts, characteristics, and applications of smart cities and provide a conceptual model of the smart city mega-events framework. Finally, we outline the impact of recent emerging technologies’ implications on challenges, applications, and solutions for futuristic smart cities.

Agriculture is the culture of land and rearing of the plants to provide food for the nourishment and enhancement of life. In India, it is one of the fundamental financial sources; various sorts of plants are cultivated each year. There are various microorganisms that cause many plant diseases and impede normal plant growth. That is the reason from long ago which led researchers to search for new methods of classification of plant diseases. Although there are different neural networks have already been used for plant disease classification, but using these methods alone do not create the best tradeoff between time and precision. So to remove this constraint, we proposed a method for plant disease classification based on Back-propagation Neural Network (BPNN) and Particle Swarm Optimization (PSO). Now we have added some more data to our dataset and applied principal component analysis to reduce the number of total features and on these features we have applied BPNN with PSO. We first train neural network using back-propagation and then we further use PSO to get more optimized weights or fine-tune the parameters of neural network. In our experiment, we have used images of leaves that are infected by various bacterial and fungal diseases: Alternaria alternata, Anthracnose, Bacterial blight, Bacterial leaf scorch, Cercospora leaf spot, and Downy mildew, and our proposed method achieves approximately 96.42 % precision.

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