论文标题
智能灌溉物联网解决方案使用神经网络的转移学习
Smart Irrigation IoT Solution using Transfer Learning for Neural Networks
论文作者
论文摘要
在本文中，我们开发了一个可靠的系统，用于使用人工神经网络和物联网体系结构来智能灌溉温室。我们的解决方案在不同的土壤层中使用四个传感器来预测未来的水分。使用数据集我们通过在不同土壤上进行实验收集的数据集，与现有的支持向量回归的替代方法相比，我们显示了神经网络的高性能。为了降低物联网边缘设备的神经网络的处理能力，我们建议使用转移学习。转移学习还可以通过少量的培训数据加快训练性能，并允许将气候传感器集成到预训练的模型中，这是温室智能灌溉的其他两个挑战。我们提出的物联网体系结构显示了一个完整的智能灌溉解决方案。
In this paper we develop a reliable system for smart irrigation of greenhouses using artificial neural networks, and an IoT architecture. Our solution uses four sensors in different layers of soil to predict future moisture. Using a dataset we collected by running experiments on different soils, we show high performance of neural networks compared to existing alternative method of support vector regression. To reduce the processing power of neural network for the IoT edge devices, we propose using transfer learning. Transfer learning also speeds up training performance with small amount of training data, and allows integrating climate sensors to a pre-trained model, which are the other two challenges of smart irrigation of greenhouses. Our proposed IoT architecture shows a complete solution for smart irrigation.
