Abstract
Satellites provide time series data in the form of multispectral images depicting land surface characteristics spanning several km2, while unmanned aerial vehicles (UAVs) provide multispectral farm data with very high resolution spanning a few hundred square meters. In contrast, low-cost sensors and IoT sensors provide accurate spatial and time series data of land and soil characteristics spanning a few meters. However, in practice, each of these data sources has been separately used even though there is scope for optimizing farm resources and improving the quality of satellite and UAV data by exploiting their complementarity. In this chapter, we present an algorithmic framework that exploits the synergies among the three data sources to construct a high-dimensional farm map. We present an outline of how this framework can help in the construction of farm map in the context of crop monitoring.
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Divakaran, S. (2023). An Algorithmic Framework for Fusing Images from Satellites, Unmanned Aerial Vehicles (UAV), and Farm Internet of Things (IoT) Sensors. In: Chaudhary, S., Biradar, C.M., Divakaran, S., Raval, M.S. (eds) Digital Ecosystem for Innovation in Agriculture. Studies in Big Data, vol 121. Springer, Singapore. https://doi.org/10.1007/978-981-99-0577-5_4
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