An Ensemble Learning Model for Agricultural Irrigation Prediction
Yanan Chen, Wen-Hao Hsieh, Yu-Shuo Ko, Nen-Fu Huang
Abstract
In agriculture, many decisions rely on the experiences of farmers, and these decisions are often difficult to quantify with simple numerical values. However, manpower has gradually been replaced by machines in the progress of agriculture. The farmers change from the laborers to decision makers for expanding planting scale or improving quality. The agricultural Internet of Things (IoT) system has become the trend of new agriculture. The data monitoring and automation control system has helped many farmers. In addition, machine learning is also widely used in the agriculture. The irrigation is the most common. The main purpose of this thesis is to effectively use agricultural IoT systems and machine learning to improve traditional agriculture. This thesis provides an ensemble learning irrigation model based on the agricultural IoT system. The IoT system provides the data collection and data monitoring functions, and also supports the website and mobile applications for the convenience of farmers. The irrigation models are also embedded in the agricultural IoT system.