Lifelong Learning for Sensor-based Precipitation Regression

该项目旨在通过降水回归优化土壤水分含量，提高小农场的作物产量。CNN或RNN将使用风速/风向，湿度，温度和气压来预测未来的降水。一旦网络经过训练，终身学习将被应用于提高网络在小型农场内的性能，使用从一系列传感器和Arduino实时收集的数据。未来的降水量将与当前的湿度水平相结合，以预测作物今天是否需要浇水。

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.