UAS/UAV or as some prefer to call them, drones, are providing us with a significant source of data primarily for agricultural applications. Over the past 5 years we have developed an impressive capability of data analytics based on the use of rotary and fixed wing platforms, multiple sensors and data processing workflows. In the UK Environment Systems has an established network of qualified and agriculturally focused operators that regularly carry out targeted flights and capture data to our own tight specifications and quality assurance procedures. We help our clients select the appropriate platform and sensor solution to meet their precise requirements.

Depending on the platform, flying at heights of up to 400 feet drones can capture up to 100ha per flight and depending on the sensors, at a ground sampled distance (GSD) of 3cm. In other words very high resolution. The sensors themselves vary too. RGB sensors deliver 3cm GSD while the multispectral sensors required for detailed crop analysis deliver 10cm GSD.

Trial plot analysis
Combining very high-resolution imagery from different sensors enables detailed data analysis and performance related indicators to help improve yield and optimise inputs

We have been extensively involved in agricultural crop trials, weed mapping, crop growth stage monitoring and modelling for yield forecasting. Many of our clients have benefited from the use of high resolution vegetation indices such as such as OSAVI, NDVI, NDRE, and GDVI for monitoring growth and development of crops at a sub-trial plot or sub-field level.

Weed or invasive species mapping such as black-grass, provides a good example of what can be achieved. We have now developed a suite of algorithms and an operational workflow for black-grass detection in winter wheat. Initially developed at trial plot level, the algorithms have been fine-tuned and scaled to function at a field-scale. The algorithms have an operational ‘sweet spot’, where best results are achieved when the black-grass is heading over the crop canopy at a significant density so that it can be detected from the imagery.

These data can be made available to suit a wide variety of farm management and other application software platforms.