An Optimized Soil Moisture Prediction Model for Smart Agriculture Using Gaussian Process Regression

• 全文链接

基于高斯过程回归的智能农业优化土壤水分预测模型

关键词：

来源：

IEEE

全文链接：

//agri.nais.net.cn/topic/downloadFile/71d67fff-4d8a-460d-b513-5ccd29bc9b70

来源地址：

//agri.nais.net.cn/topic/downloadFile/71d67fff-4d8a-460d-b513-5ccd29bc9b70

资源所属：

农业水土资源监测专题

类型：

会议论文

语种：

英语

原文发布日期：

2022-05-27

摘要：

An accurate soil moisture model is critical in the design and implementation of a smart agriculture system. Accurate soil moisture prediction allows an efficient water resources allocation. This paper presented a soil moisture model using different environmental parameters such as humidity, temperature, light intensity, and rain occurrence as inputs or predictor variables. Gaussian process regression algorithm, a non-parametric machine learning algorithm, was used to develop the model. The most effective kernel function was also determined by developing four different GPR models using a different kernel function. In terms of RMSE, the rational quadratic function obtained the lowest value. To further improve the accuracy of the GPR model, an automated hyperparameter tuning was done using a Bayesian optimization algorithm. Three hyperparameters were tuned using the Bayesian optimization algorithm, which improved the GPR model's performance. The optimized GPR model achieved the lowest RMSE and MAE of 3.596 and 1.176, respectively.