缅北轮奸

 and his research team from the  are part of an international consortium[1] selected to gain a greater understanding of plant science, pest and disease by collecting large amounts of observation data to boost farming resilience and food security.

As the global population continues to grow, China and other countries have been developing technologies to help evaluate soil and crop health in a bid to increase food production, and ultimately help produce more for less.

To ensure that the rising demand for food can be met, Professor Chen and his colleagues  and  are researching how to improve unmanned autonomous ground and air vehicles – such as fixed wing aircraft or quadrotors – in order to fulfil remote data collection requirements. This includes relaying timely and repetitive information about crops spanning a wide area, and enabling the technologies to be operated with less expertise.

The project titled  has been jointly funded by the  under the Newton Fund.

It is believed that precision agriculture or ‘smart farming’ as it is also known will increase the quality and quantity of agricultural production by using sensing technology to make farms more connected. Examples include the measure of soil moisture and nutrition using airborne sensors on unmanned aircraft, automatic detection of weeds with airborne cameras, and co-ordination with autonomous mechanical weeders for treatment. These technologies can be used to identify and destroy weeds that threaten crops and help to reduce pressure on China’s already damaged environment.

Professor Chen said: “Agriculture is facing serious challenges around the world due to increasing and ageing populations as well as a growing global demand for energy and fresh water. The likelihood of extreme weather events occurring more often also threatens food production, which is why remote data collection to evaluate soil and crop health has an important role to play in developing sustainable agriculture for rapidly developing countries like China.

“This data can be utilised in a variety of ways including for the early detection of diseases and pests, but there is still work to be done in ensuring unmanned ground and air vehicles can cover vast areas to return more data without hefty operation costs and reliance upon the operator’s experience and skills. It is our intention that this project will improve strategic and tactical decision making by developing computer algorithms to improve overall farm management. Targeted interventions and site specific treatments will be made possible thanks to better data collection and analysis.”