Seeed Studio's SenseCAP AI Aims to Break Down the Barriers to Making Sensor Data Actionable
Fill in a form and a textual prompt will have a pre-built large language model (LLM) sift through your data for the information you need.
Seeed Studio has announced the launch of SenseCAP AI, which aims to use artificial intelligence algorithms to manage sensor data — more easily and quickly extracting actionable information from the company's SenseCAP family of devices, by constructing natural-language prompts for a large language model (LLM).
"We are excited to announce the launch of SenseCAP AI, our AI-powered sensor data analysis solution designed to help you make the most of your sensor data," says Seeed Studio's Lily Li of the launch. "With SenseCAP AI, you can easily analyze the data collected by your SenseCAP sensors, gain actionable insights, and optimize your operations for improved efficiency and reduced costs."
Designed to connect to sensors in Seeed's own SenseCAP family and launching with a focus on environmental monitoring, the deep-learning system uses a large language model (LLM) interface to allow the user to "talk" to the system in natural language to pose queries and request automated analyses of submitted data. The textual prompts aren't entered by hand, however, but generated based on the user's form entries and linked sensor data — increasing the likelihood of a good-quality response, but hampering the platform's flexibility.
Functionality shown off in Seeed's demo of the platform includes having the system perform statistical analysis — automatically discarding an outlier in the data in the demo — and draw on a database of domain knowledge to provide advice on agricultural pest control, planting, and technical advice, to the point of even recommending particular apple cultivars to match the conditions reported in the sensor data.
"SenseCAP AI is now available on the SenseCAP Portal and SenseCAP Mate App," Li says of the rollout, which at the time of writing focused on environmental monitoring devices only, "offering users easy access to AI-powered sensor data analysis at any time. Our user-friendly platform and app make it simple to connect your sensors and start receiving valuable insights in just minutes."
The company has released a step-by-step tutorial on using the tool alongside its general availability — though anyone wishing to follow along will need one or more devices from the SenseCAP environmental monitoring range, such as the S-series weather stations, soil moisture and temperature sensor, carbon dioxide sensor, or light intensity sensor.
Third-party data sources do not appear to be supported at the time of writing, and the model focuses exclusively on agriculture and animal husbandry applications, with more functionality to be added as the platform matures.
More information is available on the Seeed wiki.