# TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
## 2 TinyML
### Applications Areas of TinyML
PlantVillage intends to employ TinyML more widely in the development of Nuru, sending microcontroller sensors to remote farms to offer better monitoring information for analysis. TinyML is also discovering new applications in the agricultural commodities chain, such as coffee beans [118]. For example, two Norwegian businesses, Roest and Soundsensing, have devised a method to automatically recognize the \"first crack\" of coffee beans during the roasting process. It is critical to identify the first crack since the time spent roasting after the first crack has a major impact on the quality and flavor of the processed beans. To accomplish this task, companies have added a microcontroller with TinyML in their bean roasting equipment, which has increased the efficiency, precision, and scalability of the coffee roasting process. Also, a farmer could with the right equipment as well as with appropriate forecasting models running for weather forecasting, know locally for that particular field its daily weather and its characteristics. * TinyML in Vehicular Services and Autonomous Vehicles: As more environmentally friendly and healthful vehicles, such as shared bicycles, electric mopeds, scooters, and so on, come into existence, current patterns in urban transportation are changing. However, due to the high energy requirements of their linked on-board units (OBUs), modern vehicle networks largely take into account conventional forms of road transportation including cars, buses, and trucks. Although various attempts to connect bicycles to C-ITS systems have been made [119], personal autos have not been taken into consideration due to their novelty and inherent limitations. The ability to integrate these devices into C-ITS and smart city ecosystems, on the other hand, is made possible by connecting them with MCU-based OBUs [120]. Thus, basic vehicle services like route planning, device status monitoring, and driving safety and so on [120] will be accessible to light cars. Autonomous driving has made giant strides since the advent of deep learning (DL). Small vehicles driving choices have historically been off-loaded to remote computers, requiring energy-intensive, time-consuming, and unreliable transfers of raw data. By processing data on-board and immediately controlling the motor controllers, off-system transfers may be avoided. However, because the system is battery-powered, only a tiny portion of the electricity can be sent toward the processing unit, which is the autonomous vehicle's \"brain\". TinyML approaches are therefore required to solve these issues and deal with on-device sensor data processing at the hardware, algorithmic, and software levels. * TinyML in Smart and Secure Societies: Water systems, a safe food supply chain [121], intelligent transportation systems, disaster relief [122], smart energy grids [123], and emergency response technologies [124], and so on [125]. Also, efficient defect detection in modern production lines, such as logistics, in numerous stages of the manufacturing process [126]. * TinyML in Intelligent New Spaces: The collaborative intelligence that now exists in scenarios like smart cities and cognitive buildings, to name a few, will be strengthened by simple objects with ML capabilities. Current IoT-based surveillance and monitoring systems, such as those used for traffic, pollution [96], and crowd identification, will grow into autonomous and intelligent entities [127] capable of making quick and decentralized choices. Because of independence from the power grid and the simplicity of installation, itemsmay be installed in isolated and rural locations, creating smart spaces [128] with smooth mobility between them. Furthermore, lower end-device costs will stimulate their adoption in underserved areas, which may assist revitalize local economies and commercial activity.