Lifelong Learning for Sensor-based Precipitation Regression

基于传感器的降水回归的终身学习

• 批准号: 2458063
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2458063
• 关键词: Lifelong; Learning; Sensor; based; Precipitation

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.

该项目旨在通过降水回归来优化土壤水分含量，从而提高小型农场的作物产量。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。一旦网络得到训练，终身学习将被应用于改善网络的性能，当网络定位在小农场内时，使用从一系列传感器和Arduino实时收集的数据。未来的降雨量将与当前的湿度水平相结合，以预测今天作物是否需要浇水。