WaruGuard: Real-time potato disease detection on Raspberry Pi using a TFLite model. Offline, fast, and farmer-friendly.
Control your home with just your voice! Our TinyML-powered offline smart automation lets you say “Lights On” to switch devices—no cloud need
TinyML Forest Ranger detects illegal logging using TinyML and LoRa on the Helium Network, enabling real-time alerts for forest protection.
Our pets deserve more to stay active. A tinyML model predicts activities based on the data coming from 3 Axis IMU.
Building an inclination estimator system with Arduino Nicla Sense ME and Neuton TinyML
Predict the possibility of arrhythmias on an 8- bit Microcontroller, without sending the corresponding sensor data to the cloud.
Developing a TinyML solution for the Shipping Industry that allows users to track the status of a shipment/package in real time.
Creating a faster, smaller, and more accurate magic wand than the famous experiment provided in Arduino Sketch, given the same hardware.
Learning Image Classification on embedding devices (ESP32-CAM)
Collecting and analyzing data to predict which F1 car will finish the race first.
Implementing sensor fusion to detect smoke and reduce false alarms.
Experimenting with air-writing recognition on Arduino’s smallest board
Applying sensor fusion with RSL10 and Bosch sensors to run a TinyML model for predictive maintenance of compressor water pumps.
Detect spoiled products using the Tiny Machine Learning approach.
Exploring Machine Learning on the tiny device of the Seeed XIAO family, the ESP32S3 Sense and SenseCraft-Web-Toolkit
Amateur's Guide: Comparing Vision AI MCUs on Ease & Speed
How to apply the TinyML approach to detect truck failure related to the Air Pressure System (APS) in a timely manner.
A fun and simple project that uses TinyML to detect and respond to dog barks.
Play Tic-Tac-Toe (also known as Xs and Os) using handwritten digits recognized with the help of TinyML techniques.
Using data from different sensors and ML on the edge, this device can detect early sign of algae bloom and notify authorities or citizens.
Classificate gestures using an Esp32, MPU6050 and Edge Impulse.
A proof-of-concept project to recognize air-written alphabet, using Arduino Nano RP2040 Connect run with Edge Impulse tinyML model.
A TinyML model to predict the Lithium Ion battery's life cycle within shorter time using Edge Impulse.
A tiny DIY cam built with Xiao ESP32S3 Sense. Captures images with a button press and saves them to an SD card.
