A Self-Optimizing System For Measuring And Predicting Soil Moisture Content And Leaf Wetness Level In Crop Fields
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