Agriculture not only plays a significant role in a nation’s economic development but is also the key to the survival of all life forms on this planet. Crop production has been immensely affected by global warming and the drastic changes in overall climate and rainfall patterns. Farmers who used to manually choose which crop to grow in a specific region given the soil, water, and atmospheric conditions can no longer do so owing to this change. Emerging technologies can be used to improve crop productivity by switching from traditional farming practices to relying on Machine Learning algorithms to monitor the soil and water quality along with the atmospheric conditions and to recommend the crops suitable to be grown based on these monitored factors. Having Internet of Things (IoT) work alongside Artificial Intelligence in our “Smart Farming System” has resulted in a powerful tool that will help farmers choose the right crop to grow. The soil parameters like soil nitrogen, soil phosphorus, soil potassium, soil moisture, and soil pH are gathered from the sensors using IoT. Similarly, atmospheric temperature is collected from another sensor using IoT. These values are fed into a chosen Machine Learning model, which performs the crop prediction. This prediction result is then sent to the mobile application, which acts as the User Interface (UI). This mobile application recommends suitable crops to farmers based on the parameter values collected from their fields and displayed on the application.

Convolutional Neural Networks (CNNs) have been explored to detect forced oscillations in windfarm systems in the past. However, these CNNs require a significant amount of data samples between inference queries and a significant amount of computational power and time. This leads to systems that have a large delay between a forced oscillation occurring and detecting the forced oscillation. This paper presents a novel approach applying Hyperdimensional Computing (HDC) as an effective solution for the first time in forced oscillation detection to overcome the problems of CNNs. HDC is able to reduce the time to detect forced oscillations in two ways: First, by reducing the time needed to collect data to create a new inference sample by reducing the number of data points required. Second, by providing a significantly smaller, more energy efficient, and faster model for detection than current state-of-the-art. Our results show that HDC, with an FPGA implementation, is able to achieve $ 55\times$ faster detection of forced oscillations in windfarms while achieving the same accuracy as the best current CNN models using software solutions.

Distributed cameras have been used widely for real-time image recognition. There are two main approaches in distributed camera systems: 1. The cameras are equipped with a powerful high-end processor for local image processing, 2. Low-cost cameras with resource-constrained processors are used for capturing the images and transferring them to a cloud server for classification purposes. The first approach is costly and not scalable. The second approach is too slow for real-time object detection due to the transfer delays to a remote server. These problems exacerbate in multi-view image recognition, where a central platform is required for collective image processing of multiple images from the same scene. So, there is a need for a scalable intelligent engine that can adapt itself based on the communication and processing delays and energy level of cameras (if battery operated) to accomplish real-time object recognition. This paper proposes to move from the traditional cognitive services based on training models in the cloud and requesting inference locally at cameras to a hierarchical bandwidth-efficient machine learning structure spread across cameras, edge devices and the cloud server for handling high density data streams.