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Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

These days cloud-based infrastructure is facing many challenges, out of which the major issue is their syncing data before cutover and data migration. Due to the limited scalability in terms of security concerns of cloud computing, the need for a centralized IoTs based environment has been constrained to a limited extent. The sensitivity of device latency emerged during healthy systems such as health monitoring, etc. is the main reason, because healthy systems require computing operations on highvolume data. Fog computing provides an innovative solution to improve the performance of cloud computing, providing the ability to take the necessary resources and those that are closer to the end-users. Existing fog computing models retain several limitations, such as either considering result accuracy or overestimating response time, but managing both together impairs system compatibility. FETCH is a proposed framework that integrates with edge computing devices to work on deep learning technology and automated monitoring and offers a highly useful framework for real-life health care systems such as heart disease and more. The proposed Fog-enabled cloud computing framework uses FogBus, which demonstrates utility in the form of consumption of power, network bandwidth, jitter, latency, process execution time, and their accuracy as well.

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