Lifelong Learning for Sensor-based Precipitation Regression

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This project seeks to improve the crop yield of a small farm by optimising the soil-moisture content via precipitation regression. A CNN or RNN, will use wind speed/direction, humidity, temperature, and air pressure to predict future precipitation. Once the network is trained, lifelong learning will be applied to improve the network's performance when localised within the small farm, using data collected in real-time from a range of sensors and an Arduino. The future precipitation will be combined with the current moisture levels to create predictions on whether the crop requires watering today.