Soil Moisture Forecasting Using Ensembles of Classifiers
作者： N. Rajathi , L. S. Jayashree
DOI: 10.1007/978-3-319-30933-0_25
关键词: Agriculture 、 Irrigation 、 Water content 、 Soil water 、 Ensemble forecasting 、 Agricultural engineering 、 Software deployment 、 Field (computer science) 、 Computer science 、 Ensembles of classifiers
摘要: In the field of agriculture, accurate and timely forecast soil moisture has great influence on crop growth cultivation. The water status an irrigated needs to be monitored regularly make effective irrigation decisions. challenge is develop a feasible method collect examine large volume data continuous base. developments in wireless technologies have made practical deployment reliable sensor nodes possible for various agricultural monitoring operations, which facilitate meet goal. historical known advance order predict future readings. This work introduces Soil Moisture Forecasting Ensemble Model (SMFEM) by combining features machine learning approaches. experimental results confirm that prediction accuracy proposed approach better when compared individual classifiers.
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