Compact optimized deep learning model for edge: a review

Most real-time computer vision applications, such as pedestrian detection, augmented reality, and virtual reality, heavily rely on convolutional neural networks (CNN) for real-time decision support. In addition, edge intelligence is becoming necessary for low-latency real-time applications to process the data at the source device. Therefore, processing massive amounts of data impact memory footprint, prediction time, and energy consumption, essential performance metrics in machine learning based internet of things (IoT) edge clusters. However, deploying deeper, dense, and hefty weighted CNN models on resource-constraint embedded systems and limited edge computing resources, such as memory, and battery constraints, poses significant challenges in developing the compact optimized model. Reducing the energy consumption in edge IoT networks is possible by reducing the computation and data transmission between IoT devices and gateway devices. Hence there is a high demand for making energy-efficient deep learning models for deploying on edge devices. Furthermore, recent studies show that smaller compressed models achieve significant performance compared to larger deep-learning models. This review article focuses on state-of-the-art techniques of edge intelligence, and we propose a new research framework for designing a compact optimized deep learning (DL) model deployment on edge devices

PLANT DYNAMICS: Triticum Infection Disclosure

Agriculture being the pillar of the economy for a developing country like India has a vital role in the survival of living beings on earth. Wheat is the most widely consumed grain on the planet. Deep learning is an evolving technology that is having a significant effect in the field of agriculture, assisting farmers in modernizing their operations. One such application is the identification of plant diseases using image classification which is necessary for long-term agriculture sustainability. Wheat plants are susceptible to a variety of fungal diseases. Hence early identification of diseases of crops like wheat and rice that are staple food of people in many countries is critical. Using deep learning algorithms such as CNN, this proposed system aims to predict wheat diseases. We are introducing a deep learning-based model for image classification to predict wheat diseases. Previous approaches used machine learning algorithms for a general dataset that included all types of crop diseases. To achieve better precision, we built our own dataset and combined it with existing similar datasets to account for 4 major classes of wheat diseases. The dataset consists of 700 images of wheat plants. Based on the input, our system determines if the plant is healthy or diseased so that precautionary measures can be taken to prevent losses in wheat cultivation, which could lead to food shortage