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