Operational unmanned aerial vehicle assisted post-emergence herbicide patch spraying in maize: A field study

Precision Agriculture 2015 - Papers Presented at the 10th European Conference on Precision Agriculture, ECPA 2015

Volumn , Issue , 2015, Pages 159-166

  Pelosi, F ^a   Castaldi, F ^a   Casa, R ^a  

^a UNIVERSITY OF TUSCIA   (Italy)

Author keywords

Imagery; NDVI; Site specific weed management; Weed distribution

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS; HERBICIDES; MICROSTRIP ANTENNAS; PRECISION AGRICULTURE; WEED CONTROL;

CENTRAL ITALY; ECONOMIC AND ENVIRONMENTAL BENEFITS; HERBICIDE APPLICATION; IMAGERY; NDVI; PRESCRIPTION MAP; WEED DISTRIBUTION; WEED MANAGEMENT;

UNMANNED AERIAL VEHICLES (UAV);

EID: 84947283203     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3920/978-90-8686-814-8_19     Document Type: Conference Paper

References (16)

1. Ballesteros, R., Ortega, J.F., Hernandez, D., Moreno, M.A. 2014. Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: application to maize and onion crops of a semiarid region in Spain. Precision Agriculture 15(6) 579-592.
2. Cardina, J., Johnson, G.A., and Sparrow, D.H. 1997. The nature and consequence of weed spatial distribution. Weed Science 45 364-373.
3. de Castro, A.I., Jurado-Exposito, M., Pena-Barragan, J.M., Lopez-Granados, F. 2012. Airborne multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops. Precision Agriculture 13(3), 302-321.
4. Hamouz, P., Hamouzova, K., Tyšer, L., and Holec, J. 2014. Effect of site-specific weed management in winter crops on yield and weed populations. Plant, Soil and Environment 60(1) 27-35.
5. Gerhards, R., and Christensen, S. 2003. Real-time weed detection, decision making and patch spraying in maize, sugar-beet, winter wheat and winter barley. Weed Research 43 385-392.
6. Goel, P.K., Prasher, S.O., Patel, R.M., Landry, J.A., Bonnell, R.B., Viau, A.A. 2003. Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Computers and Electronics in Agriculture 39(2) 67-93.
7. Gutierrez, P.A., Lopez-Granados, F., Pena-Barragan, J.M., Jurado-Exposito, M., Hervas-Martinez, C. 2008. Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data. Computers and Electronics in Agriculture 64(2) 293-306.
8. Hsu, C.W., Chang C.C., Lin C.J. 2010. A practical guide to support vector classification. National Taiwan University. http://ntu.csie.org/~cjlin/papers/guide/guide.pdf.
9. Lopez-Granados, F. 2011. Weed detection for site-specific weed management: mapping and real-time approaches. Weed Research 51(1) 1-11.
10. Nordmeyer, H. 2006. Patchy weed distribution and site-specific weed control in winter cereals. Precision Agriculture 7(3) 219-231.
11. Oerke, E.C. (2005). Crop losses to pests. Journal of Agricultural Science 144(1) 31-43.
12. Pena, J. M., Torres-Sanchez, J., de Castro, A. I., Kelly, M., and Lopez-Granados, F. (2013). Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PloS One 8(10) e77151.
13. Rango, A., Laliberte, A.S., Steele, C., Herrick, J.E., Bestelmeyer, B., Schmugge, T., et al. 2006. Using unmanned aerial vehicles for rangelands: Current applications and future potentials. Environmental Practice 8 159-168.
14. Torres-Sanchez, J., Pena, J.M., de Castro, A.I., Lopez-Granados, F. 2014. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Computers and Electronics in Agriculture 103 104-113.
15. Weiss, M., 2002. EYE-CAN User Guide. NOV-3075-NT-1260. NOVELTIS, Toulouse, France.
16. Zanin, G., Ferrero, A., and Sattin, M. 2011. La gestione integrata delle malerbe: un approccio sostenibile per il contenimento delle perdite di produzione e la salvaguardia dell'ambiente (The integrated management of weeds: a sustainable approach for containment of production losses and environmental protection). Italian Journal of Agronomy 6(2) 31-38.