Monitoring agricultural ecosystems

The world’s population is predicted to reach nearly 10 billion by 2050, and at the same time, economic growth and improving living standards in developing countries are driving up food consumption. To accommodate these increasing demands for food, the agricultural sector will need to produce at least 50% more food by 2050. The increasing food production will need to be accomplished not only on degrading soils, with depleting freshwater resources and while experiencing climate change but also sustainably to ensure long-term food and water security. With little existing space to expand current agricultural extents, the increased food production needs to be realized within existing farms through sustainable intensification of farming and by ensuring increased yield (FAO, 2017; Hunter et al., 2017; Karthikeyan et al. 2020). Through the history of agriculture, we have witnessed three major revolutions, that is, transitioning from hunting and gathering to planting; increasing productivity of farming through mechanization; and introducing genetic engineering, hybrid plants, and application of fertilizers and pesticides. To meet the growing global demand for food, a new revolution is required to further increase food production. This new revolution is considered by many to be digital agriculture (Shepherd et al., 2018). Digital agriculture is focusing on management nd decision support infrastructure, including new sensing systems installed in situ or on robotics and unmanned aerial system (UAS). With an increasing number of miniaturized devices and sensors, data collection is becoming quicker, easier, and more accurate. As an integral part of digital agriculture, artificial intelligence and other improved data processing and intelligent software solutions are assisting in analyzing and making sense of an ever-increasing amount of data for agricultural production. With improvements in information and communication technology and increased connectivity, real-time or near real-time information is becoming available to improve decision-making and farm management, all of which can help enhance food production efforts (Fountas et al., 2020). Here we will focus on one aspect of this digital resolution in green farming: the procurement of spatially rich and temporally dense records of on-farm behavior via the use of UAS-based sensing technologies. UAS-based data collection has a unique advantage over other sensing systems due to the flexibility of deployment, the ability to cover specified spatial extents, ease of access, and the provision of consistent information. As such, UAS-based sensing technologies are playing a key role in advancing the promise of digital agriculture to facilitate data collection and actionable information useful for farm management and increased food production