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The commercial market for drone technology applications across the electricity, gas, mining and agriculture sector could spark a £1.1bn boost to UK GDP by 2030 according to new research from PwC.
The UK report – ‘Skies without limits’ – analyses the broader economic impact from drone technology, with a particular focus on seven sector groupings from manufacturing and construction to transport and logistics. It reveals significant opportunities for economic gains, with the overall uplift in drone usage potentially growing UK GDP by £42 billion (or 2%) by 2030.
While more than 76,000 drones1 are expected to take to UK skies over this period, as many as 25,700 could be employed across the electricity, gas, mining and agriculture sector grouping.
From flame-throwing drones used to clear rubbish from power lines and pre-investment planning geospatial surveys to monitoring construction projects, it’s becoming increasingly apparent how this cutting-edge technology can positively impact the energy industry, in particular.
Steve Jennings, PwC’s UK energy leader, commented
“The UK energy industry is at the early stages of drone adoption, and the projects we are seeing tend to have very specific objectives. As businesses gain experience with this technology, we expect to see more evidence of the accumulation of drone collected data across wider programmes and tighter integration with other sources of management data.
“As well as being a more efficient way of gathering standardised, tangible data than people on the ground or in manned aerial vehicles, it’s worth noting that they can also do it in a fraction of the time and without risking human life. That is of particular benefit across mining, gas and electricity sectors. And by automating routine tasks, improving effectiveness, safety and reducing costs, drones will free up people to focus on higher-value work.
“For utilities, just owning drone equipment will not be a differentiator – it’s how they then apply drones-captured image data in fields such as power plants, electrical substations or power lines and continue to innovate that will allow them to gain a competitive advantage.”
The analysis also reveals that rising use of drone technology has the potential to save the UK up to £16bn in costs by 2030 through increasing productivity, with the electricity, gas, mining and agriculture sector set to benefit by £0.1bn.
However, it is worth noting that the level of sector savings could be conservative for two key reasons:
In addition, as many as 4,800 drones could be employed in the construction and manufacturing sector, and could boost UK GDP by £8.6bn by 2030.
In the construction sector, drones are already providing cheap and efficient ways to map sites and track construction progress against schedule and the original design as well as inspecting structures for ongoing wear and tear. They also offer an effective method of collecting three-dimensional information, integrating it with existing building information modeling (BIM) systems.
Elaine Whyte, UK drones leader at PwC, commented:
“Drones have the potential to offer a powerful new perspective for businesses across a variety of industries, delivering both productivity benefits and increased value from the data they collect. The UK has the opportunity to be at the leading edge of exploiting this emerging technology, and now is the time for investments to be made in developing the use cases and trial projects needed to kickstart our drone industry.
“I envisage that the advantages of drone technology will be well established within the decade – not only for business purposes, but also for helping to protect our society, for example, through being used by the emergency services. There is a need for current UK drone regulation to advance to see the estimations in our report become a reality, but it’s positive to see the Government already taking proactive steps to address this with the draft Drones Bill.
“In order to realise the full potential from drones, the immediate focus must be on developing society’s confidence in the technology to help drive acceptance and increase adoption. While drones are often currently viewed as more of a toy, by combining this emerging technology with the right business understanding and human insight there is a huge opportunity to help solve some of business and society’s most important problems.”
Drones have seen significant uptake in the oil and gas industry. They improve safety, increase efficiency and deliver significant cost savings. Drones have dramatically reduced employee exposure to ‘working at height’ (the number two cause of industrial fatalities in UK in 2017) and to other hazardous environments such as the inside of storage tanks.
Combined with traditional techniques, drones can dramatically increase the efficiency of inspection. For an underdeck inspection on an oil platform, a drone can complete in five days what would take eight weeks with a traditional scaffolding approach. Rope access crews only then need attend to the defective areas identified by the drone inspection.
The ability of drones to inspect while an asset is ‘live’ delivers noteworthy cost avoidance. Inspecting a live flare stack can save £4M+ per day compared to shutting the asset down for traditional inspection.
Other applications include pipeline inspection, gas sensing, oil spill monitoring, delivery, environmental/ wildlife monitoring and security. The next steps are for drones to become autonomous and mirror the development of subsea drones (remotely operated vehicle or ROV) to, for example, use mechanical arms to take wall thickness readings at an area of corrosion.
Unlike remotely operated vehicle (ROV) developments of the last decades, drone developments are taking place at a time when we are moving towards the Fourth Industrial Revolution.
What could emerge is a convergence of technologies where each oil and gas facility has a compliment of aerial, ground and subsea robotics that autonomously inspect and repair based on deviations that robotic or other Internet of Things (IoT) sensors identify against the baseline digital model held by the facilities AI.
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