Honey bee colonies are essential for global food security and ecosystem stability, yet they face escalating threats from pests, diseases, and environmental stressors. Traditional hive inspections are labor-intensive and disruptive, while cloud-based monitoring solutions remain impractical for remote or resource-limited apiaries. Recent advances in Internet of Things (IoT) and Tiny Machine Learning (TinyML) enable low-power, real-time monitoring directly on edge devices, offering scalable and non-invasive alternatives. This survey synthesizes current innovations at the intersection of TinyML and apiculture, organized around four key functional areas: monitoring hive conditions, recognizing bee behaviors, detecting pests and diseases, and forecasting swarming events. We further examine supporting resources, including publicly available datasets, lightweight model architectures optimized for embedded deployment, and benchmarking strategies tailored to field constraints. Critical limitations such as data scarcity, generalization challenges, and deployment barriers in off-grid environments are highlighted, alongside emerging opportunities in ultra-efficient inference pipelines, adaptive edge learning, and dataset standardization. By consolidating research and engineering practices, this work provides a foundation for scalable, AI-driven, and ecologically informed monitoring systems to support sustainable pollinator management.

TinyML is a generic approach for shrinking AI models and applications to run on smaller devices, including microcontrollers, cheap CPUs and low-cost AI chipsets. While most AI development tools focus on building bigger and more capable models, deploying TinyML models requires developers to think about doing more with less. TinyML applications are often designed to run on battery-constrained devices with milliwatts of power, a few hundred kilobytes of RAM and slower clock cycles. Teams need to do more upfront planning to meet these stringent requirements. TinyML app developers need to consider hardware, software and data management and how these pieces will fit together during prototyping and scaling up. ABI Research predicts the number of TinyML devices will grow from 15.2 million shipments in 2020 to a total of 2.5 billion by 2030.

TinyML reduces the complexity of adding AI to the edge, enabling new applications where streaming data back to the cloud is prohibitive. One common factor for all these applications is the low cost and power usage of the hardware they run on. Sure, we can detect audio and visual wake words or analyze sensor data for predictive maintenance on a desktop computer. But, for a lot of these applications to be viable, the hardware needs to be inexpensive and power efficient (so it can run on batteries for an extended time). Fortunately, the hardware is now getting to the point where running real-time analytics is possible.

If you're interested in embedded machine learning (TinyML) on the Arduino Nano 33 BLE Sense, you'll have found a ton of on-board sensors -- digital microphone, accelerometer, gyro, magnetometer, light, proximity, temperature, humidity and color -- but realized that for vision you need to attach an external camera. In this article, we will show you how to get image data from a low-cost VGA camera module. We'll be using the Arduino_OVD767x library to make the software side of things simpler. You can of course get a board without headers and solder instead, if that's your preference. The one downside to this setup is that (in module form) there are a lot of jumpers to connect.

This post was originally published by Sandeep Mistry and Dominic Pajak on the TensorFlow blog. Arduino is on a mission to make machine learning simple enough for anyone to use. We've been working with the TensorFlow Lite team over the past few months and are excited to show you what we've been up to together: bringing TensorFlow Lite Micro to the Arduino Nano 33 BLE Sense. In this article, we'll show you how to install and run several new TensorFlow Lite Micro examples that are now available in the Arduino Library Manager. The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands.

This post was originally published by Sandeep Mistry and Dominic Pajak on the TensorFlow blog. Arduino is on a mission to make machine learning simple enough for anyone to use. We've been working with the TensorFlow Lite team over the past few months and are excited to show you what we've been up to together: bringing TensorFlow Lite Micro to the Arduino Nano 33 BLE Sense. In this article, we'll show you how to install and run several new TensorFlow Lite Micro examples that are now available in the Arduino Library Manager. The first tutorial below shows you how to install a neural network on your Arduino board to recognize simple voice commands.