Run TensorFlow models on edge devices

On the Edge

Machine learning has changed the computing paradigm. Products today are built with artificial intelligence (AI) as a central attribute, and consumers are beginning to expect automation and human-like interactions with the devices they are using. However, much of the deep learning revolution has been limited to the cloud. Recently, and thanks to the performance increase of microcontroller and embedded devices, several machine learning libraries for mobile, embedded, and Internet of Things (IoT) devices have been announced that aim to offload computation to the edge. One such toolset is TensorFlow Lite, which I use in a real-world vertical farming operation.

Although building and training deep neural networks requires powerful servers provided with graphics processing units (GPUs) or tensor processing units (TPUs), machine learning inference requires fewer resources and can be executed on the edge. Developers can add intelligence to IoT devices that perform tasks such as anomaly detection, speech recognition, or regression (i.e., making a prediction), without depending on a cloud and thus wireless connectivity. Edge computing is so much more scalable, because the computation cost is spread over the end devices, instead of being centralized in a cloud. For some use cases, like speech recognition, AI on the edge guarantees better privacy, because personal data is kept on the device in your home or office.

In this article, I focus on the use of TensorFlow Lite [1] on IoT devices and, more precisely, in the application domain of vertical farming. Machine learning and artificial intelligence will help optimize vegetable yield or predict the harvesting date of plants.

The first section briefly introduces the concept of vertical farming before introducing TensorFlow Lite and looking at how it differs from TensorFlow. Then, I present the connected vertical farm and why and how it is used with

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