In recent years, unmanned aerial vehicles with onboard spectral sensors have been used in detecting diseases in the agricultural fields. Geolocation, i.e. calculating the global coordinate of identified diseased regions based on images taken, is an important step in automating such a scouting operation. In this paper, the problem of geolocating multiple diseased regions in an image is studied. Based on the assumptions of stationary, two-dimensional shallow target plants and hover flight, an orthographic projection-based measurement model is developed. A probabilistic data association method is used to analyze the measurements from different target sources and a Kalman filter is designed to estimate the suspected diseased leaves’ position. To the best of the authors’ knowledge, it is the first time a data association technique is used in for locating multiple-diseased plants in agriculture applications. Additionally, the designed Kalman filter is based on conditions pertinent to small crops and is less computationally intensive than the typically used extended Kalman filter. Both simulation and ad hoc experiments are used to validate the proposed multitarget geolocation algorithm.
DefterliSGShiYXuYet al.Review of robotic technology for strawberry production. Appl Eng Agric2016; 32: 301–318.
2.
Costa FG, Ueyama J, Braun T, et al. The use of unmanned aerial vehicles and wireless sensor networks in agricultural applications. In: IEEE international geoscience and remote sensing symposium, Munich, Germany, 22–25 July 2012, pp.5045–5048. New York: IEEE.
3.
YangCEverittJHBradfordJM. Airborne hyperspectral imagery and yield monitor data for estimating grain sorghum yield variability. Trans ASAE2004; 47: 915–924.
4.
Zaman B, Jensen AM and McKee M. Use of high-resolution multispectral imagery acquired with an autonomous unmanned aerial vehicle to quantify the spread of an invasive wetlands species. In: IEEE international geoscience and remote sensing symposium, Vancouver, BC, Canada, 24–29 July 2011, pp.803–806. New York: IEEE.
5.
Xu Y, Ehsani R, Kaplan J, et al. An octo-rotor ground network for autonomous strawberry disease detection – Year 1 update. In: 2nd international conference on robotics and associated high-technologies and equipment for agriculture and forestry, Madrid, Spain, 21–23 May 2014, pp.457–466.
6.
BerniJAJZarco-TejadaPJSuarezLet al.Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Trans Geosci Remote Sens2009; 47: 722–738.
7.
PanLZhangWZhuNet al.Early detection and classification of pathogenic fungal disease in post-harvest strawberry fruit by electronic nose and gas chromatography-mass spectrometry. Food Res Int2014; 62: 162–168.
8.
Ehsani R, Sankaran S, Maja JM, et al. Affordable multi-rotor remote sensing platform for applications in precision horticulture. In: 12th international conference on precision agriculture, Sacramento, CA, USA, 20–23 July 2014.
9.
Teke M, Deveci HS, Haliloglu O, et al. A short survey of hyperspectral remote sensing applications in agriculture. In: 6th international conference on recent advances in space technologies, Istanbul, Turkey, 12–14 June 2013, pp.171–176.
10.
Gomez-CandonDLopez-GanadosFCaballero-NovellaJJet al.Geo-referencing remote images for precision agriculture using artificial terrestrial targets. Precis Agric2011; 12: 876–891.
11.
XiangHTianL. Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform. Biosyst Eng2011; 108: 104–113.
12.
Garcia C and Xu Y. Target geolocation for disease detection applications via an octorotor. In: 5th IFAC conference on sensing, control and automation for agriculture – AgriControl 2016, Seattle, WA, USA, 14–17 August 2016.
13.
Savage CO and La Scala BF. Accurate target geolocation using cooperative observers. In: 2007 IEEE information, decision, and control conference, Adelaide, Australia, 12–14 February 2007, pp.248–253. New York: IEEE.
14.
Wu YA. EO target geolocation determination. In: Proceedings of the 34th IEEE conference on decision and control, New Orleans, LO, 13–15 December 1995, pp.2766–2771. New York: IEEE.
15.
Campbell ME and Wheeler M. A vision based geolocation tracking system for UAVs. In: Proceedings of the 2006 AIAA guidance, navigation, and control conference and exhibit, Keystone, CO, 21–22 August 2006, pp.1942–1959. Washington DC: AIAA.
16.
SohnSLeeBKimJet al.Vision based real-time target localization for single-antenna GPS-guided UAV. IEEE Trans Aerosp Electron Syst2008; 44: 1391–1401.
17.
SzeliskiR. Computer vision: Algorithms and applications, New York: Springer Science & Business Media, 2010.
18.
Sawhney HS and Hanson AR. Identification and 3D description of ‘shallow’ environmental structure in a sequence of images. In: Proceedings of the 1991 IEEE computer society conference on computer vision and pattern recognition, Maui, HI, 3–6 June 1991, pp.179–185. New York: IEEE.
19.
Bar-ShalomYFortmannTE. Tracking and data association, San Diego, CA: Academic Press, 1988.
20.
Bar-ShalomYDaumFHuangJ. The probabilistic data association filter: Estimation in the present of measurement origin uncertainty. IEEE Control Syst Magaz2009; 29: 82–100.
21.
LiNXuYBassetGet al.Tracking the trajectory of space debris in close proximity via a vision-based method. J Aero Eng2014; 27: 238–248.
22.
Bar-ShalomY. Tracking methods in a multitarget environment. IEEE Trans Autom Control1978; 23: 618–626.
23.
Colegrove SB and Davey SJ. On using nearest neighbours with the probabilistic data association filter. In: The record of the IEEE 2000 international radar conference, Alexandria, VA, USA, 7–12 May 2000, pp.53–58. New York: IEEE.
24.
Oh S and Sastry S. An efficient algorithm for tracking multiple maneuvering targets. In: Proceedings of the 44th IEEE conference on decision and control and 2005 Euro control conference, Seville, Spain, 12–15 December 2005, pp.4010–4015. New York: IEEE.
25.
YechoutTRMorrisSLBossertDEet al.Introduction to aircraft flight mechanics: Performance, static stability, dynamic stability, and classical feedback control, New York: AIAA Education Series, 2003.
26.
SimonD. Optimal state estimation: Kalman filter, h infinity, and nonlinear approaches, Hoboken, NJ: John Wiley & Sons, 2006.
