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Network simulation is a method of using software or a tool which can be used to mimic the network conditions that exist in pre-defined places. This kind of simulation allows the developers, designers, researchers and the network planners to intelligently plan, design, develop and test their applications or research work in changing network conditions. With varying network conditions either because of wireless nature or because of user mobility, it is very difficult to simulate the exact network conditions with the existing network simulators. These network simulators are flexible, re-usable and reliable. But they have a limitation of not being able to replicate the actual network conditions in the laboratories. This calls for a system in the loop or hardware in the loop concept to be extended to the network simulators. The idea of system in the loop is not new. In this paper, an overview with the fundamental understanding of the hardware-in-loop concept for network simulators, their applications and a review of the existing hardware-in-loop network simulators with their advantages and disadvantages is presented.

Plant diseases are one of the major factors affecting crop yield. Early identification of these diseases can improve productivity and save money and time for the farmer. This paper presents a novel technique to diagnose plant diseases using a mobile application. A Convolutional Neural Network (CNN) model was built and trained using MobileNetV2 architecture with the help of image processing techniques and transfer learning. A dataset comprising 87,000 images that contain 38 classes of diseases belonging to 14 different crops was used to train the model. The model achieved an accuracy of 98.69% and a loss of 0.5373. A mobile application was built in Android Studio with the help of a trained model. The mobile application built works without a need for a remote server. The application can identify the disease, gives information regarding the identified disease and also suggests necessary remedies to tackle the disease.