IoT-Based NPK Monitoring and Management System

The IoT-Based NPK Monitoring and Management System is a cutting-edge project designed to revolutionize agriculture and gardening practices by leveraging the power of the Internet of Things (IoT). This innovative system focuses on the real-time monitoring and optimization of Nitrogen (N), Phosphorus (P), and Potassium (K) levels in soil, crucial for plant health and growth. Utilizing a network of IoT sensors strategically placed in the soil, the system continuously collects data on NPK levels and other environmental factors. This information is then transmitted to a centralized platform, accessible through a user-friendly interface. Farmers or gardeners can remotely monitor the nutrient levels and receive timely insights into the soil's health.

IoT-Based Vehicle Pollution detector and sms alert

The IoT-Based Vehicle Pollution Detector with SMS Alert is an innovative solution designed to address environmental concerns by monitoring and controlling vehicle emissions. This project focuses on measuring the amount of pollution in vehicle exhaust and automatically alerting vehicle owners when pollution levels exceed a predefined threshold. The system incorporates real-time data collection, analysis, and instant communication to promote cleaner air and responsible driving practices.

What is TinyML?

TinyML, short for Tiny Machine Learning, refers to the practice of deploying machine learning models on small, resource-constrained devices, often at the edge of the network. The term "tiny" reflects the goal of running machine learning algorithms on devices with limited processing power, memory, and energy resources.

Training on Larger Platforms: While the inference happens on the edge, the training of TinyML models typically occurs on more powerful hardware, such as traditional computers or servers. Once trained, the models are then converted and optimized for deployment on edge devices. Popular frameworks and tools for TinyML include TensorFlow Lite for Microcontrollers, Edge Impulse, and others that provide the necessary tools to train, convert, and deploy machine learning models on resource-constrained devices. TinyML plays a crucial role in extending the capabilities of small devices, making them smarter and more responsive to the surrounding environment. It opens up new possibilities for applications that require on-device intelligence, improved privacy, and reduced dependence on cloud services.
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