Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery

Agronomy for Sustainable Development

Volumn 36, Issue 4, 2016, Pages

  López Granados, Francisca ^a   Torres Sánchez, Jorge ^a   De Castro, Ana Isabel ^a   Serrano Pérez, Angélica ^a   Mesas Carrascosa, Francisco Javier ^b   Peña, José Manuel ^a  

^a INSTITUTO DE AGRICULTURA SOSTENIBLE   (Spain)

^b UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Corn; Drone; Johnsongrass; Maize; OBIA; Precision agriculture; Site specific herbicide; Sorghum halepense; UAS; Weed detection and mapping

Indexed keywords

AERIAL SURVEY; AGRICULTURAL LAND; COST ANALYSIS; GRASS; HERB; HERBICIDE; IMAGE ANALYSIS; MAIZE; MAPPING METHOD; PESTICIDE APPLICATION; PHENOLOGY; PRECISION AGRICULTURE; SPATIAL DISTRIBUTION; SURFACE AREA; VEGETATIVE REPRODUCTION; VIDEOGRAPHY; WEED;

POACEAE; SORGHUM HALEPENSE; ZEA MAYS;

EID: 85007492450     PISSN: 17740746     EISSN: 17730155     Source Type: Journal    
DOI: 10.1007/s13593-016-0405-7     Document Type: Article

References (30)

1. Andújar D, Ribeiro Á, Fernández-Quintanilla C, Dorado J (2011) Accuracy and feasibility of optoelectronic sensors for weed mapping in wide row crops. Sensors 11:2304–2318. doi:10.3390/s110302304
2. Andújar D, Ribeiro A, Fernández-Quintanilla C, Dorado J (2013) Herbicide savings and economic benefits of several strategies to control Sorghum halepense in maize crops. Crop Prot 50:17–23. doi:10.1016/j.cropro.2013.04.003
3. Armstrong J-JQ, Dirks RD, Gibson KD (2007) The use of early season multispectral images for weed detection in corn. Weed Technol 21:857–862. doi:10.1614/WT-06-074.1
4. Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65:2–16. doi:10.1016/j.isprsjprs.2009.06.004
5. Castillejo-González IL, Peña-Barragán JM, Jurado-Expósito M et al (2014) Evaluation of pixel- and object-based approaches for mapping wild oat (Avena sterilis) weed patches in wheat fields using QuickBird imagery for site-specific management. Eur J Agron 59:57–66. doi:10.1016/j.eja.2014.05.009
6. De Castro AI, Jurado-Expósito M, Peña-Barragán JM, López-Granados F (2012) Airborne multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops. Precis Agric 13:302–321. doi:10.1007/s11119-011-9247-0
7. De Castro AI, López Granados F, Jurado-Exposito M (2013) Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control. Springer. Precis Agric 14:392–413. doi: 10.1007/s11119-013-9304-y
8. Gómez-Candón D, Castro AID, López-Granados F (2014) Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precis Agric 15:44–56. doi:10.1007/s11119-013-9335-4
9. Gómez-Casero MT, Castillejo-González IL, García-Ferrer A et al (2010) Spectral discrimination of wild oat and canary grass in wheat fields for less herbicide application. Agron Sustain Dev 30:689–699. doi:10.1051/agro/2009052
10. Guerrero JM, Guijarro M, Montalvo M et al (2013) Automatic expert system based on images for accuracy crop row detection in maize fields. Expert Syst Appl 40:656–664. doi:10.1016/j.eswa.2012.07.073
11. Haarbrink RB, Eisenbeiss H (2008) Accurate DSM production from unmanned helicopter systems. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVII:1259–1264
12. Hengl T (2006) Finding the right pixel size. Comput Geosci 32:1283–1298. doi:10.1016/j.cageo.2005.11.008
13. Lopez-Granados F (2011) Weed detection for site-specific weed management: mapping and real-time approaches. Weed Res 51:1–11. doi:10.1111/j.1365-3180.2010.00829.x
14. López-Granados F, Peña-Barragán JM, Jurado-Expósito M et al (2008) Multispectral classification of grass weeds and wheat (Triticum durum) using linear and nonparametric functional discriminant analysis and neural networks. Weed Res 48:28–37. doi:10.1111/j.1365-3180.2008.00598.x
15. López-Granados F, Torres-Sánchez J, Serrano-Pérez A et al (2016) Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds. Precis Agric 17:183–199. doi:10.1007/s11119-015-9415-8
16. Meier U (2001) Growth stages of mono- and dicotyledonous plants. BBCH Monograph. Federal Biological Research Centre for Agriculture and Forestry
17. Montalvo M, Pajares G, Guerrero JM et al (2012) Automatic detection of crop rows in maize fields with high weeds pressure. Expert Syst Appl 39:11889–11897
18. Otsu N (1979) A threshold selection method from gray-level histograms. IEE Trans Syst Man Cybern 9:62–66
19. Pajares G (2015) Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogramm Eng Remote Sens 81:281–330. doi:10.14358/PERS.81.4.281
20. Peña JM, Torres-Sánchez J, de Castro AI et al (2013) Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS One 8:e77151. doi:10.1371/journal.pone.0077151
21. Peña JM, Torres-Sánchez J, Serrano-Pérez A et al (2015) Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors 15:5609–5626. doi:10.3390/s150305609
22. Rey-Caramés C, Diago MP, Martín M, Lobo A, Tardaguila J (2015) Using RPAS multi-spectral imagery to characterise vigour, leaf development, yield components and berry composition variability within a vineyard. Remote Sens 7(11):14458–14481. doi:10.3390/rs71114458
23. Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS, NASA SP-351. NASA Sp-351 I, Washington, DC, USA, pp 309–317
24. Thomlinson JR, Bolstad PV, Cohen WB (1999) Coordinating methodologies for scaling. Landcover classifications from site-specific to global: steps toward validating global map products. Remote Sens Environ 70:16–28. doi:10.1016/S0034-4257(99)00055-3
25. Torres-Sánchez J, López-Granados F, De Castro AI, Peña-Barragán JM (2013) Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS One 8:e58210. doi:10.1371/journal.pone.0058210
26. Torres-Sánchez J, Peña JM, de Castro AI, López-Granados F (2014) Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput Electron Agric 103:104–113. doi:10.1016/j.compag.2014.02.009
27. Torres-Sánchez J, López-Granados F, Peña JM (2015) An automatic object-based method for optimal thresholding in UAV images: application for vegetation detection in herbaceous crops. Comput Electron Agric 114:43–52. doi:10.1016/j.compag.2015.03.019
28. Vasilakoglou I, Dhima K, Eleftherohorinos I (2005) Allelopathic potential of bermudagrass and johnsongrass and their interference with cotton and corn. Agron J. doi:10.2134/agronj2005.0303
29. Wan H, Wang Q, Jiang D et al (2014) Monitoring the invasion of Spartina alterniflora using very high resolution unmanned aerial vehicle imagery in Beihai, Guangxi (China). Sci World J 2014:e638296. doi:10.1155/2014/638296
30. Woebbecke DM, Meyer GE, Von Bargen K, Mortensen DA (1995) Color indices for weed identification under various soil, residue, and lighting conditions. Trans Am Soc Agric Eng 38:259–269. doi:10.13031/t1995.2013