Invasive Spear Thistle Weed Detection and Zoysia grass Mapping Using UAV Aerial Imagery

Transcription

1 Invasive Spear Thistle Weed Detection and Zoysia grass Mapping Using UAV Aerial Imagery Bilal Alsalam, Felipe Gonzalez Queensland University of Technology ARC Centre of Excellence Robotic Vision

2 Overview Motivation System Architecture Detection and GPS Mapping Approach Study Site Results Conclusions

3 Motivation:Autonomous Low cost UAV with Vision Based Onboard Decision Making Page 3

4 Motivation Spear Thistle (Cirisium vulgare) is a spiny, large headed, purple flowered annual to biennial thistle to m. Abundance in South-Eastern Queensland,the southern and eastern parts of New South Wales, the ACT, Victoria and Tasmania. In Victoria alone, this type of weed infested around 9.7 million hectares in 1993 (Australian Government, 2016, Australian Weeds Committee, 2016). In New Zealand, each Spear Thistle per square metre was estimated to reduce sheep live weight gain by 1.68 kg (HerbiGuide, 2016). The spines of Spear Thistle may transfer infections such as scabby mouth in sheep and myxomatosis in rabbits (HerbiGuide, 2016). The estimated cost of the weed control used by Australian farmers is approximately around $1.5 billion a year and more than $2.5 billion a year in lost agricultural 50 production due to weeds (Australian Government, 2016). Early detection, green on green detection, eradication

5 System Architecture

6 System Architecture (2)

7 Detection and GPS Mapping Approach This figure describes the process flow of data and information developed in this work from the UAV take off to display the GPS location of the each weed in a table. Sensitivity: is the ability of the algorithm to identify and detect the true positive target. Selectivity: is the capability of the algorithm to filter out the false negatives for detection targets.

8 Weed Detection Algorithm The detection algorithm implemented and a threshold selection method was used. Image processing stages for Spear Thistle detected using OBIA and colour threshold.

9 Weed Detection Algorithm Spear Thistle weed detection and classification at 1 and 3 m height above ground at different stages of growth.

10 Study Site Aerial image for the experimental field showing manually labelled the Spear thistle weed, dead Zoysia grass and green Zoysia grass.

11 UAV Flight Imagery Experimental site and area. A: where UAV aerial images were taken, image locations in blue and the UAV flight path in red area covered by image in yellow; B: Unmanned quadrotor-type aerial vehicle flying above experimental field.

12 Results Algorithm accuracy at three different altitudes. Altitude (m) No. Of images Camera Resolution Ground sampling Distance (GSD) Sensitivity (%) Selectivity (%) pixel= cm 95% 98% pixel= cm 90% 94.5% pixel=0.4875cm 80% 85% The classification algorithm managed to provide classified results with 95% sensitivity and 98% selectivity at 5 m, 90% sensitivity and 94.5% selectivity at 7 m and 80% sensitivity and 85% selectivity at 15 m.

13 Results (2) Spear Thistle weeds before and after detection at 5 m height above ground. Spear Thistle weeds before and after detection at 15 m height above ground.

14 Results (3) This figure illustrates the location and mapping of the Spear Thistle weeds. The blue dots represent the Spear Thistle weeds and the red dots represent the GPS coordinates. Spear Thistle weeds mapping with GPS coordinates at 5 m altitude

15 Results (4) GPS Latitude and Longitude for detected weeds. No. Latitude Longitude

16 Conclusions and Future Work The algorithm developed for Spear Thistle weed detection is effective at identifying weeds from Zoysia grass and soil. Results demonstrated that our system is capable of target detection to 95% sensitivity and 98% selectivity at 5m height above ground, 90% sensitivity and 94.5% selectivity at 7 m height above ground and 80% sensitivity and 85% selectivity at 15m height above ground with precise GPS mapping. The GPS tabulated locations of weed detection and the weed map provides useful information that can be used in decision-making systems to calculate herbicide requirements and estimate the overall cost of weed management operations. Future work will involve the development on an on-board system for Site-specific weed management and spraying herbicide. In this work we concentrate on low cost RGB camera sensor.

17 Acknowledgements Plant Biosecurity CRC Michael Cahill Farmer, Christmas Creek, Beaudesert, QLD

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