Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery

Remote Sensing of Environment

Volumn 198, Issue , 2017, Pages 105-114

  Jin, Xiuliang ^a   Liu, Shouyang ^a   Baret, Frédéric ^a   Hemerlé, Matthieu ^b   Comar, Alexis ^b  

^a UAPV   (France)

^b Hi Phen   (France)

Author keywords

Computer vision algorithm; Particle swarm optimization (PSO) support vector machine (SVM); Plant density; Unmanned aerial vehicle; Winter wheat

Indexed keywords

AGRICULTURE; CAMERAS; CROPS; PARTICLE SWARM OPTIMIZATION (PSO); PIXELS; PLANTS (BOTANY); SENSITIVITY ANALYSIS; SUPPORT VECTOR MACHINES; UNMANNED AERIAL VEHICLES (UAV);

COMPUTER VISION ALGORITHMS; EFFECT OF THE WIND; FLIGHT ALTITUDES; GROUND RESOLUTION; HIGH RESOLUTION IMAGERY; HIGH-THROUGHPUT PHENOTYPING; PLANT DENSITIES; WINTER WHEAT;

IMAGE PROCESSING;

ALGORITHM; COMPUTER VISION; CROSS SECTION; EMERGENCE; ESTIMATION METHOD; IMAGE ANALYSIS; OPTIMIZATION; PERFORMANCE ASSESSMENT; PIXEL; SOFTWARE; SOWING DATE; SUPPORT VECTOR MACHINE; WHEAT; ZENITH ANGLE;

AUSTRALIA; AVIGNON; CLERMONT; FRANCE; PROVENCE-ALPES-COTE D'AZUR; QUEENSLAND; VAUCLUSE;

TRITICUM AESTIVUM;

EID: 85020307153     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.06.007     Document Type: Article

References (45)

1. Agisoft, L.L.C., Agisoft Photoscan User Manual Professional Edition, Version 1.2. 2016, Agisoft LLC, St. Petersburg, Russia Available at: http://www.agisoft.com/pdf/photoscan-pro_1_2_en.pdf (Accessed: 11.03.2016).
2. Ahmed, S.K., Abidin, A.A.Z., Ali, Z., Wong, B.Y., Shariff, Z.M., Image processing of a banana: area determination via edge detection using MATLAB. 4th International Colloqium on Signal Processing and Its Applications, Kuala Lumpur, 2008.
3. Araus, J.L., Cairns, J.E., Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19 (2014), 52–61.
4. Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M.L., Bareth, G., Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. 39 (2015), 79–87.
5. Díaz-Varela, R.A., de la Rosa, R., León, L., Zarco-Tejada, P.J., High-resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: application in breeding trials. Remote Sens. 7 (2015), 4213–4232.
6. Ehsani, R., Maja, J.M., The rise of small UAVs in precision agriculture. Resour. Mag. 20 (2013), 18–19.
7. Furbank, R., Tester, M., Phenomics - technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 16 (2011), 635–644.
8. Gómez-Candón, D., De Castro, A., López-Granados, F., Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precis. Agric. 15 (2014), 44–56.
9. Guillen-Climent, M., Zarco-Tejada, P.J., Berni, J.A., North, P., Villalobos, F., Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV. Precis. Agric. 13 (2012), 473–500.
10. Haralick, R.M., Shapiro, L.G., Computer and Robot Vision, 1. 1992, Addison-Wesley, 28–48.
11. Haun, J.R., Visual quantification of wheat development. Agron. J. 65 (1973), 116–119.
12. Hough, P.V.C., 1962. Method and means for recognizing complex patterns. U.S. Patent 3069654.
13. Hunt, E.R., Cavigelli, M., Daughtry, C.S., Mcmurtrey, J.E. III, Walthall, C.L., Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precis. Agric. 6 (2005), 359–378.
14. Hunt, E.R., Hively, W.D., Daughtry, C.S.T., McCarty, G.W., Fujikawa, S.J., Ng, T.L., Linden, D.S., Yoel, D.W., Remote sensing of crop leaf area index using unmanned airborne vehicles. Proceedings of the Pecora 17 Symposium, Denver, CO, 2008.
15. Hunt, E.R., Hively, W.D., Fujikawa, S.J., Linden, D.S., Daughtry, C.S.T., McCarty, G.W., Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sens. 2 (2010), 290–305.
16. Jia, J., Krutz, G.W., Gibson, H.W., Corn plant locating by image processing, Fibers' 91, Boston, MA. Int. Soc. Opt. Photon., 1991, 246–253.
17. Jin, J., Tang, L., Corn plant sensing using real-time stereo vision. J. Field Robot. 26 (2009), 591–608.
18. Joseph, K., Alley, M., Brann, D., Gravelle, W., Row spacing and seeding rate effects on yield and yield components of soft red winter wheat. Agron. J. 77 (1985), 211–214.
19. Kenndy, J., Eberhart, R., Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, 1995, 1942–1948.
20. Liu, S.Y., Baret, F., Andrieu, B., Burger, P., Hemmerlé, M., Estimation of wheat plant density at early stages using high resolution imagery. Front. Plant Sci., 8, 2017, 739, 10.3389/fpls.2017.00739.
21. Maertens, K., Reyns, P., De Clippel, J., De Baerdemaeker, J., First experiments on ultrasonic crop density measurement. J. Sound Vib. 266 (2003), 655–665.
22. Meyer, G.E., Neto, J.C., Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 63 (2008), 282–293.
23. Mountrakis, G., Im, J., Ogole, C., Support vector machines in remote sensing: a review. ISPRS J. Photogramm. 66 (2011), 247–259.
24. Nakarmi, A.D., Tang, L., Automatic inter-plant spacing sensing at early growth stages using a 3D vision sensor. Comput. Electron. Agric. 82 (2012), 23–31.
25. Nakarmi, A.D., Tang, L., Within-row spacing sensing of maize plants using 3D computer vision. Biosyst. Eng. 125 (2014), 54–64.
26. Otsu, N., A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9 (1979), 62–66.
27. Saeys, W., Lenaerts, B., Craessaerts, G., De Baerdemaeker, J., Estimation of the crop density of small grains using LiDAR sensors. Biosyst. Eng. 102 (2009), 22–30.
28. Sankaran, S., Khot, L.R., Carter, A.H., Field-based crop phenotyping: Multispectral aerial imaging for evaluation of winter wheat emergence and spring stand. Comput. Electron. Agric. 118 (2015), 372–379.
29. Sankaran, S., Khot, L.R., Espinoza, C.Z., Jarolmasjed, S., Sathuvalli, V.R., Vandemark, G.J., Miklase, P.N., Carterf, A.H., Pumphrey, M.O., Knowles, N.R., et al. Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. Eur. J. Agron. 70 (2015), 112–123.
30. Shi, Y., Wang, N., Taylor, R.K., Raun, W.R., Improvement of a ground-LiDAR-based corn plant population and spacing measurement system. Comput. Electron. Agric. 112 (2015), 92–101.
31. Shrestha, D.S., Steward, B.L., Automatic corn plant population measurement using machine vision. Trans. ASAE, 46, 2003, 559.
32. Shrestha, D.S., Steward, B.L., Shape and size analysis of corn plant canopies for plant population and spacing sensing. Appl. Eng. Agric. 21:2 (2005), 295–303.
33. Sullivan, D., Fulton, J., Shaw, J., Bland, G., Evaluating the sensitivity of an unmanned thermal infrared aerial system to detect water stress in a cotton canopy. Trans. ASABE 50 (2007), 1963–1969.
34. Tang, L., Tian, L.F., Plant identification in mosaicked crop row images for automatic emerged corn plant spacing measurement. Trans. ASABE 51 (2008), 2181–2191.
35. Tang, L., Tian, L.F., Real-time crop row image reconstruction for automatic emerged corn plant spacing measurement. Trans. ASABE 51 (2008), 1079–1087.
36. Vapnik, V., The Nature of Statistical Learning Theory. 2013, Springer Science & Business Media.
37. Vapnik, V.N., Vapnik, V., Statistical Learning Theory, 1. 1998, Wiley, New York.
38. Verger, A., Vigneau, N., Chéron, C., Gilliot, J.M., Baret, F., Green area index from unmanned aerial system over wheat and rapeseed crops. Remote Sens. Environ. 152 (2014), 654–664.
39. Walter, A., Liebisch, F., Hund, A., Plant phenotyping: from bean weighing to image analysis. Plant Methods 11 (2015), 1–11.
40. Whaley, J., Sparkes, D.L., Foulkes, M.J., Spink, J.H., Semere, T., Scott, R.k., The physiological response of winter wheat to reductions in plant density. Ann. Appl. Biol. 137 (2000), 165–177.
41. Willmott, C.J., Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 11 (1982), 1303–1313.
42. Wilson, P.D., Sagan, C., Spectrophotometry and organic matter on Iapetus: 1. Composition models. J. Geophys. Res. Planets 100 (1995), 7531–7537.
43. Zarco-Tejada, P.J., González-Dugo, V., Berni, J.A., Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 117 (2012), 322–337.
44. Zarco-Tejada, P.J., Guillén-Climent, M.L., Hernández-Clemente, R., Catalinac, A., Gonzálezc, M.R., Martínc, P., Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Agric. For. Meteorol. 171 (2013), 281–294.
45. Zhang, C., Kovacs, J.M., The application of small unmanned aerial systems for precision agriculture: a review. Precis. Agric. 13 (2012), 693–712.