Population growth and economic expansion lead to exponentially increasing demands for agricultural output at a global scale. At the same time, agriculture production has a bidirectional relation with climate as it impacts and is impacted by climatic changes.
To face these challenges, agriculture must evolve to anticipate and effectively adapt to these changes and variations, while focusing on food sustainability and security.
Modern technological solutions allow the collection of variegated data and information from a combination of sensing and measuring devices. For example, satellite remote sensing allows the continuous coverage of wide areas at a low cost. Similarly, proximal sensing from low-flying unmanned aerial vehicles (UAVs) and drones collects fine-grained information near the surface.
Additionally, data from in-situ sensors like spectrometers, information from weather stations, and open datasets that expose historical data on weather, climate and soil, are equally valuable data sources for the involved stakeholders.
Consequently, farmers, scientists and agricultural policy makers have a vast data pool to be analysed towards taking informed decisions. For example, in irrigated agriculture, precipitation and temperature monitoring during various stages of the growing season allow farmers to more efficiently plan the timing of water application and apply the amount of water needed to optimize crop yields.
Using heterogeneous data fusion techniques, high-throughput stream processing and spatio-temporal time series analysis, we offer real-time visual analytics for:
- environmental conditions (e.g. temperature, humidity, wind, solar radiation, leaf wetness)
- agricultural parameters (e.g. evapotranspiration, green biomass, leaf area index)
- soil erosion, desertification and land degradation
- crop and grassland area estimates
- change detection of agricultural lands and grasslands