Paper Title - A Self-Optimizing System For Measuring and Predicting Soil Moisture Content and Leaf Wetness Level in Crop Fields


Abstract
Global crop irrigation consumes a substantial amount of the world’s freshwater withdrawals. Optimizing water usage efficiency in agricultural practices becomes a priority in ensuring global water and food security. This paper presents the design of a system which will assist crop field managers in determining the optimal irrigation schedule for their crops by providing real-time information on the soil moisture content and leaf wetness levels of their crop fields. Additionally, the system predicts the next day soil moisture content and leaf wetness levels using a self-optimizing support vector machine regression algorithm. Ultimately, the results of this system will assist crop field managers in optimizing the water usage efficiency of their crops. Keywords- Machine Learning; Weather Prediction; Precision Agriculture;Local Weather Station; Internet of Things

Author - Thomas Truong, Pham Son, Anh Dinh

Citation - Thomas Truong   ,   Pham Son   ,   Anh Dinh   ,   Thomas Truong, Pham Son, Anh Dinh " A Self-Optimizing System For Measuring and Predicting Soil Moisture Content and Leaf Wetness Level in Crop Fields " , International Journal of Industrial Electronics and Electrical Engineering (IJIEEE) , Volume-5,Issue-8  ( Aug, 2017 )

Indexed - Google Scholar

IRAJ DOI Number - IJIEEE-IRAJ-DOI-8844

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