Using the Mavic 2 Pro drone for basic water quality assessment

• Main Authors: Emmanuel Captain Vellemu, Vincent Katonda, Harold Yapuwa, Gomezyani Msuku, Saulosi Nkhoma, Chandiwira Makwakwa, Kingston Safuya, Alfred Maluwa
• Format: Article
• Language: English
• Published: Elsevier 2021-11-01
• Series: Scientific African
• Subjects: Ecology; Drone; Technology; Unmanned aerial vehicle (UAV); Water quality
• Online Access: http://www.sciencedirect.com/science/article/pii/S2468227621002830

This paper assessed the capability of the Dà-Jiāng Innovations (DJI) Mavic 2 Pro Drone (unmanned aerial vehicle – UAV) for the collection and delivery of river water samples for basic water quality assessments. The primary objective of this paper was to evaluate how this UAV model could help in generating large water quality data sets in the developing world to assist in the design and implementation of water quality monitoring and assessment programs, which are often a challenge due to data paucity and resources. We hypothesized that the traditional approach (portable hand meters) to measuring in-situ water parameters, including pH, dissolved oxygen, electrical conductivity, and turbidity could not yield significant water quality data variations from those collected by the Mavic 2 Pro. The UAV was equipped with a plastic bottle attached to a three-meter rigid thin line for sample collection. Samples were collected at stations 50 m apart over a 300 m river length. The drone captured samples in wind conditions of about 10.1 km/h with ease. About 350 mL of samples were collected per mission. A paired t-test was performed to determine the parameter differences between the two approaches. We conclude that, given similar environmental, physical conditions and pilot experience, Mavic 2 Pro can generate large and much more reliable datasets at faster rates than the traditional approach. The drone also avoided obstacles with ease, a perfect technology for use in rural rivers. However, pilot efficiency and precision, including agitation during flight require further investigations considering their potential parameter influences. Similar future tests should investigate the performance of this drone model and data reliability over a long river course to ascertain its capability and suitability in various conditions in ecological applications.