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Precision Agriculture (PA) and especially Remote Sensing (RS) technologies have become more affordable and popular in recent years. Cameras have become more compact and lighter, with improved spectral and spatial resolutions. The use of incoming light sensors allows for images to be automatically calibrated to surface reflectance on the fly, while the image processing software keeps getting faster and increasingly user-friendly. This progress has promoted a lot of advancements in the application of unmanned aerial vehicles (UAVs) to agricultural research, for instance within the plant breeding industry for high throughput field phenotyping. Similarly, open access satellite systems such as Sentinel-1 and 2, as well as those of private companies (e.g. Planet) make it now possible to use their data streams to regularly monitor crops and develop decision support tools for crop management. UAV and satellite based remote sensing technologies have been increasingly used in agricultural research at different scales, from experimental plots on research stations to farmers’ fields and regional scales, and brings important synergies between both systems – often ignored. A few examples of R4D using RS technologies at different spatio-temporal scales for agricultural research, development of decision support systems for farmers and its business model are going to be presented aiming comments and discussion.
Francelino has been working in Precision Agriculture and Remote Sensing since 2004, including his MSc, PhD and Postdoctoral studies. During his scientific trajectory He obtained experience with geospatial technologies in contrasting agricultural systems (coffee, sugarcane, cereals and lately, livestock pasture-based systems) in a range of countries from different continents (Latin America, South Asia, East Africa and Oceania), working at different spatio-temporal scales - from plot experiments towards small and commercial farms’ systems and regional scales. His research focuses on the analysis and interpretation of spatial and temporal agricultural data sets, built up by the use of proximal and remote sensing technologies. He has used extensively different sensing technologies for field surveys retrieving biophysical and biochemical vegetative and soil traits, enabling scalable high-throughput field phenotyping for fostering the development of decision support systems for stakeholders and end-users from different primary industries. www.linkedin.com/in/farjr
Massimiliano Pasqui