The value of precision for image-based decision support in weed management

Precision Agriculture

Volumn 18, Issue 3, 2017, Pages 366-382

  Franco, Camilo ^a   Pedersen, Søren Marcus ^a   Papaharalampos, Haris ^a   Ørum, Jens Erik ^a  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Expert knowledge; Image analysis; Image based viewpoint; Precision spraying; Precision value; Weed recognition

Indexed keywords

AERIAL PHOTOGRAPHY; DECISION MAKING; DECISION SUPPORT SYSTEM; EXPERT SYSTEM; HERBICIDE; IMAGE ANALYSIS; PRECISION AGRICULTURE; SPRAY; WEED CONTROL;

EID: 85016106850     PISSN: 13852256     EISSN: 15731618     Source Type: Journal    
DOI: 10.1007/s11119-017-9520-y     Document Type: Article

References (34)

1. Anglund, E. A., & Ayers, P. D. (2003). Field evaluation of response times for a variable rate (pressure-based and injection) liquid chemical applicator. Applied Engineering in Agriculture, 19, 273–282.
2. Bárdossy, A., & Samaniego, L. (2002). Fuzzy rule-based classification of remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 40, 362–374.
3. Benz, U. C., Hofmann, P., Willhauck, G., Lingenfelder, I., & Heynen, M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. Photogrammetry & Remote Sensing, 58, 239–258.
4. Bongiovanni, R., & Lowenberg-Deboer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5, 359–387.
5. Bullock, D. S., Kitchen, N., & Bullock, D. G. (2007). Multidisciplinary teams: A necessity for research in precision agriculture systems. Crop Science, 47, 1765–1769.
6. Fountas, S., Wulfsohn, D., Blackmore, B. S., Jacobsen, H. L., & Pedersen, S. M. (2006). A model of decision-making and information flows for information-intensive agriculture. Agricultural Systems, 87, 192–210.
7. Franco, C. (2014). On the analytic hierarchy process and decision support based on fuzzy-linguistic preference structures. Knowledge-Based Systems, 70, 203–211.
8. Franco, C., Pedersen, S. M., Papaharalampos, H., & Ørum, J. E. (2015). An image-based decision support methodology for weed management. In J. V. Stafford (Ed.), Precision agriculture’15 (pp. 595–601). Netherlands: Wageningen.
9. Grisso, R., Alley, M., Thomason, W., Holshouser, D. & Roberson, G.T. (2011). Precision Farming Tools: Variable-Rate Application. Resource document: Virginia Cooperative Extention. https://pubs.ext.vt.edu/442/442-505/442-505_PDF.pdf.
10. Gutjahr, C., & Gerhards, R. (2010). Decision rules for site-specific weed management. In E. C. Oerke, R. Gerhards, G. Menz, & R. A. Sikora (Eds.), Precision crop protection—the challenge and use of heterogeneity (pp. 223–239). Netherlands: Springer.
11. Heermann, D. F., Hoeting, J., Thompson, S. E., Duke, H. R., Westfall, D. G., Buchleiter, G. W., et al. (2002). Interdisciplinary irrigated precision farming research. Precision Agriculture, 3, 47–61.
12. Hemming, J., & Rath, T. (2001). Computer-vision-based weed identification under field conditions using controlled lighting. Journal of Agricultural Engineering Research, 78, 233–243.
13. Jensen, H. G., Jacobsen, L. B., Pedersen, S. M., & Tavella, E. (2012). Socioeconomic impact of widespread adoption of precision farming and controlled traffic systems in Denmark. Precision Agriculture, 13, 661–677.
14. Kitchen, N. R. (2008). Emerging technologies for real-time and integrated agriculture decisions. Computers and Electronics in Agriculture, 61, 1–3.
15. Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: theory and applications. New Jersey: Prentice Hall.
16. Lucas, R., Rowlands, A., Brown, A., Keyworth, S., & Bunting, P. (2007). Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping. ISPRS Journal of Photogrammetry & Remote Sensing, 62, 165–185.
17. Lund, I., Christensen, S., Jensen, L. A., Jensen, P. K., Olsen, H. J., Søgaard, H. T. et al. (2008) Cellesprøjtning af ukrudt i majs. Pesticide research document. Resource document: Ministry of Environment of Denmark. http://www2.mst.dk/udgiv/publikationer/2008/978-87-7052-851-1/978-87-7052-851-1/pdf/978-87-7052-852-8.pdf.
18. Lund, I., Søgaard, H. T. & Graglia, E. (2006) Micro-spraying with one drop per weed plant. Proceedings of Plantekongres 2006, Arhus, Denmark, January 10–11, 2006.
19. Meyer, G. E., Neto, J. C., Jones, D. D., & Hindman, T. W. (2004). Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Computers and Electronics in Agriculture, 42, 161–180.
20. Middeldatabasen (2017) SEGES—Knowledge centre for agriculture. Resource document: www.middeldatabasen.dk.
21. Pedersen, S. M. (2003). Precision farming—technology assessment of site-specific input application in cereals. DTU: IPL.
22. Pedersen, S. M., Fountas, S., Blackmore, S., Gylling, M., & Pedersen, J. L. (2004). Adoption and perspectives of precision farming in Denmark. Acta Agriculturæ Scandinavica Section B, Soil and Plant Science, 54, 2–6.
23. Peña, J. M., Torres-Sánchez, J., Castro, A. I., Kelly, M., & López-Granados, F. (2013). Weed mapping in early season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE, 8, 1–11.
24. Rasmussen, J., Nielsen, J., Garcia-Ruiz, F., Christensen, S., & Streibig, J. C. (2013). Potential uses of small unmanned aircraft systems (UAS) in weed research. Weed Research, 53, 242–248.
25. Slaughter, D. C., Giles, D. K., & Downey, D. (2008). Autonomous robotic weed control systems: A review. Computers and Electronics in Agriculture, 61, 63–78.
26. Sorensen, C. G., Pesonen, L., Fountas, S., Suomi, P., Bochtis, D., Bildsøe, P., et al. (2010). A user-centric approach for information modelling in arable farming. Computers and Electronics in Agriculture, 73, 44–55.
27. Stafford, J. V., & Miller, P. C. H. (1993). Spatially selective application of herbicide to cereal crops. Computers and Engineering in Agriculture, 9, 217–229.
28. Tagarakis, A., Liakos, V., Fountas, S., Koundouras, S., & Gemtos, T. A. (2013). Management zones delineation using fuzzy clustering techniques in grapevines. Precision Agriculture, 14, 18–39.
29. Thompson, J. F., Stafford, J. V., & Miller, P. C. H. (1991). Potential for automatic weed detection and selective herbicide application. Crop Protection, 10, 254–259.
30. van de Zande, J. C., Achten, V. T., Kempenaar, C., Michielsen, J. M., van der Schans, D., de Boer, J., et al. (2009). SensiSpray: site-specific precise dosing of pesticides by on-line sensing. In E. J. van Henten, D. Goense, & C. Lokhorst (Eds.), Precision agriculture’09 (pp. 785–792). Netherlands: Wageningen.
31. Walter, A. M. & Heisel, T. (2001) Precision application of herbicides using injection sprayer systems. In G. Grenier, S. Blackmore (Eds.), Proceedings of the 3rd European Conference on Precision Agriculture (pp. 611–616). France: agro Montpellier.
32. Whelan, B. M., & McBratney, A. B. (2001). The “null hypothesis” of precision agriculture management. Precision Agriculture, 2, 265–279.
33. Zadeh, L. A. (1975). Fuzzy logic and approximate reasoning. Synthese, 30, 407–428.
34. Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research, 14, 415–421.