The current trend of research in cloud computing is challenging and keenly marginal to understand for the researchers and the learners due to its over-cover and complementary nature towards the new technologies. Cloud Computing is part and parcel of its applications with a majority of the extending concepts of Fog Computing, Internet of Things (IoT) and Big Data Analytics. In the current scenario, it is difficult for a researcher to identify the domain applicability and interrelationship among all the specified areas. In view of this, the current study is planned to focus on the research requirements and the basics for majorly two categories of stakeholders -the learners and the researchers exploring the needs of users to cater by cloud and also the current research trends in cloud computing.

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In the Indian Economy, agriculture plays a main role, therefore prior detection of plant diseases will aid in maximizing the productivity of the crops thereby adding to the economy’s augmentation. To predict the plant diseases, manual identification is used earlier but it requires vast manpower and wide knowledge about plants. Multi disease models and pest prediction can be automated using image processing techniques. This paper shows an overview of various image processing techniques to obtain and organize diseases in the plant. Developing contamination, supplement lack, dry season, and so on are the reasons in light of which plants are inclined to the various sicknesses. Illnesses can be found on the root, stem, branches, leaves, blossoms, and organic products. Diseases in plants are the main production and financial losses also decrease in agricultural product quality and quantity. To propose a proper solution for relating illness, recognizable proof, and arrangement of infections is significant. We reviewed a simple plat leaf disease detection system that would ease progressions in agriculture. The survey on various classification techniques presented in this paper for plant leaf diseases. An Automatic finding of plant diseases is significant to regularly find out the disease signs as soon as they emerge on the mounting phase.

Internet of Things (IoT) is a combination of hardware and software technology that produces trillions of data by connecting multiple devices and sensors with the cloud and computing and accessing the required data through intelligent means of connecting and utilizing various tools. With numerous connected devices and appliances, the smart home is one of the emphasized areas of IoT. Smart home concept deals with inter connecting the working of multiple devices using IoT. In view of this, multiple home appliances are operated using the common remote controller access. When IoT devices are meant to operate based on remote control there is an immense role of identifying the state dependencies of various devices. If one device is in ON state, then it will show the status of other devices also whether to be in ON state or OFF state or choice of none of devices to be in state of ON or OFF. Till now, state dependencies of devices in home appliances are manually identified. Therefore, in order to control devices of home appliances with in a precise location, a design issue based on identification of state dependencies by using graph matrices for multiple devices can be made for better utilization to save energy and also to restrict the unnecessary access of devices.

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