Many industries have the need to identify current and past weather conditions. The data helps them plan, organize, and/or optimize their operations. For example, farmers might look at the current weather to decide if the sprinklers should be turned on or off. Ski resort operators might choose to enable snowmaking machines based on varying weather conditions across the mountain. Construction workers might plan out the supplies and rain gear they’ll need for a remote job site.
Using machine learning (ML) offers the potential to automate this by providing a digital eye. For example, a camera feed on a farm could be processed by an ML model deployed on an IoT device at the edge (e.g., on a smart camera). That model can then be used to automatically determine the current weather conditions and enable or disable sprinkler valves accordingly.
To demonstrate this use case, we built a model in PerceptiLabs trained to classify four different types of weather. We used 1,123 images from the Multi-class Weather Dataset: cloudy, shine, sunrise, and rain. Here are the results!
You can find more details on our GitHub!