Row and water front detection from UAV thermal-infrared imagery for furrow irrigation monitoring

Long, Derek and McCarthy, Cheryl and Jensen, Troy (2016) Row and water front detection from UAV thermal-infrared imagery for furrow irrigation monitoring. In: 2016 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2016), 12-15 July 2016, Banff, AB, Canada.

Abstract

Water efficiency in furrow irrigation has been improved by the introduction of feed-back sensing systems, which help inform the decision on when to cut the water off for optimal use, but typically only a limited number of furrows can be monitored using existing sensors. The aim of this research is to develop automatic machine vision algorithms for UAV (also known as Remotely Piloted Aircraft, or RPA) thermal imagery, collected as the UAV traverses overhead of a cotton crop, to monitor furrow irrigation progress of large areas of a field. An algorithm was developed for overhead thermal imagery of a cotton field with high canopy closure. A test flight with a < 2kg multirotor UAV was performed in late February, 2016 to assess the accuracy of the algorithm. It was found that at lower sensing heights (20 m), most water fronts were being detected, with a significant drop in performance at the higher altitude of 30 m. The algorithm also estimated the row direction and spacing relative to the camera, and used the estimates to calculate the row number for each detected front. The average error in water front position estimation was between 1.3 and 2 m which is well within limits for practical irrigation management. The water stream was found to be visually discernible in all crop rows captured in the overhead thermal imagery, despite the water stream not being visually discernible in overhead color imagery captured in the same UAV flights, due to the level of canopy closure.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted Version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering
Date Deposited: 11 Jun 2017 23:53
Last Modified: 20 Jun 2017 04:43
Uncontrolled Keywords: image edge detection, cameras, irrigation, unmanned aerial vehicles, sensors, cotton
Fields of Research : 09 Engineering > 0999 Other Engineering > 099901 Agricultural Engineering
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Socio-Economic Objective: B Economic Development > 82 Plant Production and Plant Primary Products > 8203 Industrial Crops > 820301 Cotton
Identification Number or DOI: 10.1109/AIM.2016.7576783
URI: http://eprints.usq.edu.au/id/eprint/29785

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