Comparison of artificial neural networks and logistic regression as potential methods for predicting weed populations on dryland chickpea and winter wheat fields of Kurdistan province, Iran

Crop Protection

Volumn 93, Issue , 2017, Pages 43-51

  Mansourian, Sahar ^a   Darbandi, Ebrahim Izadi ^a   Rashed Mohassel, Mohammad Hassan ^a   Rastgoo, Mehdi ^a   Kanouni, Homayoun ^b  

^a FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

^b Education and Extension Organization (AREEO)   (Iran)

Author keywords

Abundance index; Geographic information system; Global positioning system; Weed distribution; Zonation maps

Indexed keywords

ABUNDANCE; AGRICULTURAL LAND; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; DEMOGRAPHY; GIS; GPS; INDEX METHOD; LEGUME; POPULATION DISTRIBUTION; POPULATION DYNAMICS; REGRESSION ANALYSIS; WEED; WHEAT; WINTER;

IRAN; KORDESTAN [IRAN];

CARTHAMUS OXYACANTHUS; CICER ARIETINUM; CONVOLVULUS ARVENSIS; GALIUM APARINE; SCANDIX; TRAGOPOGON; TRITICUM AESTIVUM; TURGENIA LATIFOLIA;

EID: 84997701895     PISSN: 02612194     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cropro.2016.11.015     Document Type: Article

References (49)

1. Abdulahi, A., Dabbagh Mohammadi Nassab, A., Nasrolahzadeh, S., Zehtab Salmasi, S., Pourdad, S.S., Evaluation of wheat-chickpea intercrops as influence by nitrogen and weed management. Am. J. Agric. Biol. Sci. 7 (2012), 447–460.
2. Bai, S.B., Wang, J., Lü, G., Zhou, P., Hou, S.S., Xu, S.N., GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. J. Geomorphol. 115 (2010), 23–31.
3. Bedia, J., Busque, J., Gutierrez, J.M., Predicting plant species distribution across an alpine rangeland in north Spain. A comparison of probabilistic methods. Appl. Veg. Sci. 14 (2011), 415–432.
4. Burk, A., Classification and ordination of plant communities of the Nauntain, Namibia. J. Veg. Sci. 12 (2001), 53–60.
5. Castro, A.I., Jurado-Expo´sito, M., Pen˜a-Barraga´n, J.M., Lo´pez-GranadosAirborne, F., Multi-spectral imagery for mapping cruciferous weeds in cereal and legume crops. Precis. Agric. 13 (2012), 302–321.
6. Caussanel, J.P., Nuisibilite’ et seuil de nuisibilite’ des mauvaises herbes dans une culture annuelle: situation de concurrence bispe’ cifique. Agronomie 9 (1989), 219–240.
7. Chalechale, Y., Minbashi Moeini, M., Shirani Rad, A.H., Preparation of weed mapping of chickpea (Cicer arietinum) fields and predict their presence in agricultural fields of Kermanshah province with using Geographic Information System (GIS). J. Weed Ecol 2 (2014), 95–111 (In Persian).
8. Chikoye, D., Ekeleme, F., Weed flora and soil seed banks in fields dominated by Imperata cylindrical in the moist savannah of West Africa. Weed Res. 41 (2001), 475–490.
9. Dale, M.R.T., Thomas, A.G., John, E.A., Environmental factors including management practices as correlates of weed community composition in spring seeded crops. Can. J. Bot 70 (1992), 1931–1939.
10. Dutoit, T., Gerbaud, E., Buisson, E., Roche, P., Dynamics of a weed community in a cereal field created after ploughing a semi natural meadow: roles of the permanent seed bank. Ecosci 10 (2003), 225–235.
11. ESRI, ArcGIS 9.2. ( Redlands, California), 2006.
12. FAO, FAO Year Book. 2013, FAO Publication.
13. Fried, G., Norton, L.R., Reboud, X., Environmental and management factors determining weed species composition and diversity in France. Agric. Ecosyst Enviro 128 (2008), 68–76.
14. Gang, M., Evaluating the power of GPU acceleration for IDW interpolation algorithm. Hindawi Publishing Corporation Sci. World J., 2014, 1–8 Article ID 171574.
15. Gonzalez-Andujar, J.L., Chantre, G.R., Morvillo, C., Blanco, A.M., Forcella, F., Predicting field weed emergence with empirical models and soft computing techniques. Weed Res., 2016, 10.1111/wre.12223.
16. Goslee, S.C., Beck, K.G., Peters, D.P.C., Distribution of Russian knapweed in Colorado: climate and environmental factors. J. Range Manag 56 (2003), 206–212.
17. Hassan, G., Khan, I., Khan, M.Z., Shah, N.H., Khan, M., Liaqatullah, M., Weed flora of chickenpea in district Lakki Marwat, NWFP, Pakistan. Sarhad J. Agric. 26 (2010), 79–86.
18. Haykin, S., Neural Networks: a Comprehensive Foundation. 1994, McMillan College Publishing Company, New York.
19. Heidari, M.D., Omid, M., Akram, A., Application of artificial neural network for modeling benefit to cost ratio of broiler farms in tropical regions of Iran. Res. J. App. Sci. Eng. Technol 3 (2011), 546–552.
20. Hosmer, D.W., Lemeshow, S., Applied Logistic Regression. 2000, Wiley, New York 307 pp.
21. Irmak, A., Jones, J.W., Batchelor, W.D., Irmak, S., Boote, k.J., Paz, j.O., Artificial neural network model as a data analysis tool in precision farming. T. Am. Soc. Agric. Biol. Eng 49 (2006), 2027–2037.
22. Kavdir, S., Discrimination of sunflower, weed and soil by artificial neural networks. Comput. Electron. Agric 44 (2004), 153–160.
23. Lamb, D.E., Brown, R.B., Remote sensing and mapping weed in crops. J. Agric. Eng. Res. 48 (2001), 117–125.
24. Lee, S., Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. J. Rem. Sens. 26 (2005), 1477–1491.
25. Lobo, J.M., Jime nez- Valverde, A., Hortal, J., The uncertain nature of absences and their importance in species distribution modeling. Ecography 33 (2010), 103–114.
26. Lopez-Granados, F., Pena-Barragan, J.M., Jurado-Exposito, M., Francisco-Fernandez, M., Cao, R., Alonso-Betanzos, A., Fontenla-Romero, O., Multispectral classification of grass weeds and wheat (Triticum durum L.) using linear and nonparametric functional discriminant analysis and neural networks. Weed Res. 48 (2008), 28–37.
27. MacNeal, B.L., Evaluation and Classification of Water Quality for Irrigation. in: Impact of Salinity on Water Resources Use and Development. 1980, Marcel-Dekker, New York.
28. Mathew, J., Jha, V.K., Rawat, G.S., Application of binary logistic regression analysis and its validation for landslide susceptibility mapping in part of Garhwal Himalaya, India. Int. J. Rem. Sens. 28 (2007), 2257–2275.
29. McCully, K.M., Simpson, G., Watson, A.K., Weed survey of Nova Scotia Lowbush (Vaccinilum angustifolium) fields. Weed Sci. 39 (1991), 180–185.
30. Medellin, R.A., Gaona, O., Seed dispersal by bats and birds in forest and disturbed habitats of Chiapas, Me'xico. Biotropica 31 (1999), 478–485.
31. Memon, R.A., Khalid, S., Mallah, A., Mirbahar, A.A., Use of GPS and GIS technology in surveying and mapping of wheat and cotton weeds in Khairpur district, Sindh, Pakistan. Pak. J. Bot. 43 (2011), 1873–1878.
32. Minbashi Moeini, M., Baghestani, M.A., Rahimian Mashhadi, H., Introducing an abundance index for assessing weed flora in survey studies. Weed Bio. Manag 8 (2008), 172–180.
33. Mitchell, K.M., Pike, D.R., Using a geographic information system (GIS) for herbicide management. Weed Technol. 10 (1996), 856–864.
34. Rashed Mohassel, M.H., Najafi, H., Akbarzadeh, M.D., Weed biology and Control. second ed., 2009, Ferdowsi University of Mashhad Press, 404 (In Persian with English summary).
35. Moran, M.S., Maas, S.J., Vanderbilt, V.C., Barnes, M., Miller, S.N., Clarke, T.R., Application of image-based remote sensing to irrigate agriculture. Ustin, S.L., (eds.) Remote Sensing for Natural Resource Management and Environmental Monitoring, 2004, John Wiley & Sons, Hoboken, NJ, 648–650.
36. Murphy, C.E., Lemerle, D., Continuous cropping systems and weed selection. Euphytica 148 (2006), 61–73.
37. Nordmeyer, H., and Dunker, M. 1999. Variable weed densities and soil properties in a weed mapping concept for patchy weed control. Proceedings of the Second European Conference on Precision Agriculture, Odense Congress Centre, Denmark: 453–462.
38. Ozdemir, A., Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey). J. Hydrol 405 (2011), 123–136.
39. Radosevich, S.R., Holt, J.S., Weed Ecology: Implications for Vegetation Management. 1984, Johnwiley and Sons, New York.
40. Tang, L., Wan, K., Cheng, C., Li, R., Wang, D., Pan, J., Tao, Y., Xie, J., Chen, F., Effect of fertilization patterns on the assemblage of weed communities in an upland winter wheat field. J. Plant Ecol 7:1 (2014), 39–50.
41. Thomas, A.G., Weed survey system used in Saskatchewan for cereal and oilseed crops. Weed Sci. 33 (1985), 34–43.
42. Torrecilla, J.S., Otero, L., Sanz, P.D., A neural network approach for thermal/pressure food processing. Food Eng. 62 (2004), 89–95.
43. Villers-Ruiz, L., Trejo-Vazquez, I., Lopez-Blanco, J., Dry vegetation in relation to the physical environment in the Baia California Peninsula, Mexico. J. Veg. Sci. 14 (2003), 517–524.
44. Walter, A.M., Christensen, S., Simmelsgaard, S.E., Spatial correlation between weed species densities and soil properties. Weed Res. 42 (2002), 26–38.
45. Wehrden, H.V., Zimmermann, H., Hanspach, J., Ronnenberg, K., Wesche, K., Predictive mapping of plant species and communities using GIS and Landsat Data in Southern Mongolian mountain range. Folia Geobot. 44 (2009), 211–225.
46. Weis, M., Gutjahr, C., Rueda Ayala, V., Gerhards, R., Ritter, C., Scholderle, F., Precision farming for weed management: techniques. Gesunde Pflanz. 60 (2008), 171–181.
47. Wiles, L., Sampling to make map for site specific weed management. Weed Sci. 53 (2005), 228–235.
48. Yang, C.C., Prasher, S.O., Landry, J.A., Ramaswamy, H.S., Development of herbicide application map using artificial neural networks and fuzzy logic. Agric. Syst. 76 (2003), 561–574.
49. Yassinsar, E.K., The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey. PhD Thesis, 2005, Department of Geometrics the University of Melbourne.