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The main objective of this study is to combine remote sensing and machinelearning to detect soil moisture content. Growing population and foodconsumption has led to the need to improve agricultural yield and to reducewastage of natural resources. In this paper, we propose a neural networkarchitecture, based on recent work by the research community, that can make astrong social impact and aid United Nations Sustainable Development Goal ofZero Hunger. The main aims here are to: improve efficiency of water usage;reduce dependence on irrigation; increase overall crop yield; minimise risk ofcrop loss due to drought and extreme weather conditions. We achieve this byapplying satellite imagery, crop segmentation, soil classification and NDVI andsoil moisture prediction on satellite data, ground truth and climate datarecords. By applying machine learning to sensor data and ground data, farmmanagement systems can evolve into a real time AI enabled platform that canprovide actionable recommendations and decision support tools to the farmers.

Prediction of Soil Moisture Content Based On Satellite Data and Sequence-to-Sequence Networks