Hostname: page-component-7b7567c8c-7pwfn Total loading time: 0 Render date: 2026-11-15T18:00:09.546Z Has data issue: false hasContentIssue false
  • English
  • Français

SELOMA: Expert System for Weed Management in Herbicide-Intensive Crops

Published online by Cambridge University Press:  12 June 2017

Lucia Stigliani and
Cosimo Resina
Lucia Stigliani
Affiliation:
Head of Computer Sci. Dep. Agrobios
Cosimo Resina
Affiliation:
Univ. Bari—Metapontum Agrobios—I-75010 Metaponto (MT), Italy
Get accessRights & Permissions [Opens in a new window]

Abstract

A practical expert system is needed to handle POST weed control in herbicide-intensive crops such as wheat, barley, oat, rye, sugarbeet, corn, and sorghum. SELOMA is an expert system having a step-by-step problem-solving procedure closely resembling what a weed management expert would follow. It is based on field surveys of weed density, and crop and weed growth stage and height. SELOMA evaluates weed competitiveness and provides weed management advice. It suggests whether or not to intervene, chemical and mechanical weed control treatments, and selects the best herbicides, including commercial formulations, costs, and optimal dosages.

Keywords

Barley, Hordeum vulgare L.corn, Zea mays L.oat, Avena sativa L.rye, Secale cereale L.sugarbeet, Beta vulgaris L.sorghum, Sorghum vulgare Pers.wheat, Triticum durum Desf.Artificial intelligencecomputer programsintegrated weed controlpractical application

Information

Type
Feature
Information
Weed Technology , Volume 7 , Issue 3 , September 1993, pp. 550 - 559
Copyright
Copyright © 1993 by the Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Literature Cited

1. Aarts, H.F.M. and De Visser, C.L.M. 1985. A management information system for weed control in winter wheat. Proc. Br. Crop Prot. Conf.—Weeds. p. 679–686.Google Scholar
2. Baandrup, M. and Ballegaard, T. 1990. Advisory computer system for weed control. Proc. Eur. Weed Res. Soc. Symp.: Integrated Weed Management in Cereals, Helsinki. p. 443–450.Google Scholar
3. Baldoni, R. and Giardini, L. 1981. Coltivazioni Erbacee. Patron Eds. 1024 p.Google Scholar
4. Baldoni, G, and Vicari, A. 1991. Attualitá e prospettive del diserbo nel mais. Inf. Fitopat. 4:21–26.Google Scholar
5. Ballegaard, T. and Haas, H. 1990. Development of a computer-based Expert System for the identification of weed seedlings. Proc. Eur. Weed Res. Soc. Symp.: Integrated Weed Management in Cereals, Helsinki. p. 435–442.Google Scholar
6. Boulanger, A. G. 1983. The expert system PLANT/cd: a case in applying the general purpose inference system ADVICE to predicting black cutworm damage in corn. M.S. Thesis, Computer Science Dep., University of Illinois, Champaign-Urbana, IL. p. 115.Google Scholar
7. Caussanel, J. P. 1986. Biological threshold assessment and postemergence weed control in wheat, corn and tomato. Weed Control in Vegetable Production—Commission of the European Communities Proc. Eur. Community Experts' Group, Stuttgart. 28-31 Oct. 1986 p. 245–256.Google Scholar
8. Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google Scholar
9. Covarelli, G. 1988. Il problema delle erbe infestanti. Ital. Agric. 1: 101–114.Google Scholar
10. Covarelli, G., Canterle, A., Catizone, P., Sparacino, A., Tei, F., Vazzana, C., and Zanin, G. 1983. Le erbe infestanti: fattore limitante la produzione agraria. Proc. IV S.I.L.M. (Italian Weed Management Society) Symp. p. 182.Google Scholar
11. Covarelli, G. and Peccetti, G. 1989. Ricerche sull'epoca di emergenza delle principali erbe infestanti nell'Italia centrale. Inf. Agrar. p. 99–103.Google Scholar
12. Covarelli, G. and Tei, F. 1987. Diserbo del frumento tenero in post-emergenza: I. Controllo delle malerbe dicotiledoni. Riv. Agron. 21: 315–320.Google Scholar
13. Covarelli, G. and Tei, F. 1987. Diserbo del frumento tenero in post-emergenza: II. Controllo delle malerbe graminacee. Riv. Agron. 21: 321–324.Google Scholar
14. Cussans, G. W. and Rolph, J. 1990. HERBMAST—A herbicide selection system for winter wheat. Proc. Eur. Weed Res. Soc. Symp.: Integrated Weed Management in Cereals, Helsinki. p. 451–457.Google Scholar
15. Dawson, J. H. 1974. Full-season weed control in sugarbeets. Weed Sci. 22:330–335.Google Scholar
16. Dindorp, U. 1990. Development of an expert system for non-chemical weed control. Beretning fra faeellesudvalget for Staten Planteavlsforsog Husdyrbrugsforsog. Proc. Workshop Expert Systems in Agricultural Research, Ebeltolf, Denmark. 1:23–26.Google Scholar
17. Doluschitz, R. and Schmisseur, W. E. 1988. Expert Systems: applications to agriculture and farm management. Comput. Electron. Agric. 2: 173–182.Google Scholar
18. Doyle, C. J., Cousens, R. D., and Moss, S. R. 1986. A model of the economics of controlling Alopecurus myosuroides Huds. in winter wheat. Crop Prot. 5:143–150.Google Scholar
19. Edwards-Jones, G., Mumford, J. D., Norton, G. A., Turner, R., Proctor, G. H., and May, M. J. 1992. A decision support system to aid weed control in sugarbeet. Comput. Electron. Agric. 7:35–46.Google Scholar
20. Fabricatore Amici, J. 1988. Sostanze Attive Autorizzate in Agricoltura, ovvero, La Difesa delle Piante nel Rispetto dell'Uomo e dell'Ambiente. p. 517.Google Scholar
21. Feigenbaum, E. A. 1981. Knowledge engineering in the 1980's. Bond, A., ed. Machine Intelligence. Infotech State of the Art Report, Series 9, 3, Pergamon Infotech Ltd., Oxford.Google Scholar
22. Fermanian, T. W., Michalski, R. S., and Katz, B. 1985. An expert system to assist turf grass managers in weed identification. Proc. Summer Comput. Conf., Chicago, IL. Am. Soc. Agric. Eng. p. 499–502.Google Scholar
23. Ferrari, C., Baldoni, G., and Tei, F. 1987. Lo studio della vegetazione infestante le colture agrarie. p. 88 in Proc. VI S.I.L.M. (Italian Weed Management Society) Symp. Google Scholar
24. Ferris, I. G., Freker, T. C., Haigh, B. M., and Durrant, S. 1992. HERBICIDE ADVISER: A decision support system to optimise atrazine and chlorsulfuron activity and crop safety. Comput. Electron. Agric. 6:295–317.Google Scholar
25. Fischer, J. and Pohlmann, J. M. 1990. HERBIDEX, an expert system for early identification of weeds. Z. Pflanzenkrankheiten und Pflanzenschutz 12:157–162.Google Scholar
26. Garvert, U., Wagner, J., Kochs, H. J., Heyland, K. U., and Steel, R. 1990. HERBEXPERT—a computer-aided system for individual herbicide recommendation in cereals. Z. Pflanzenkrankheiten und Pflanzenschutz 12:191–195.Google Scholar
27. Gerowitt, B. 1990. A decision model for weed control in cereals according to economic thresholds. Proc. Eur. Weed Res. Soc. Symp.: Integrated Weed Management in Cereals, Helsinki. p. 467–474.Google Scholar
28. Gerowitt, B. and Heiterfuss, R. 1990. Weed economic thresholds in cereals in the Federal Republic of Germany. Crop Prot. 9:323–331.CrossRefGoogle Scholar
29. Glent, H. 1990. SEP - winter barley + HERBY - two programs as decision support for weed control in winter cereals. Z. Pflanzenkranheiten und Pflantsenschutz 12:171–177.Google Scholar
30. Gonzales-Andujar, J. L., Rodriguez, J., and Navarrete, L. 1990. Development of a prototype Expert System (SIEXMAL) for identification of weeds in cereals. Proc. Eur. Weed Res. Soc. Symp.: Integrated Weed Management in Cereals, Helsinki. p. 429–434.Google Scholar
31. Harrison, S. R. 1991. Validation of agricultural expert systems. Agric. Systems 35:265–285.Google Scholar
32. Kigel, J. and Koller, D. 1985. Asexual reproduction of weeds. in Duke, S. O., ed. Weed Physiology. Vol. I, CRC Press, Boca Raton, FL.Google Scholar
33. Linker, H. M., York, A. C., and Wilhite, D. R. Jr. 1990. WEEDS—A system for developing a computer-based herbicide recommendation program. Weed Technol. 4:380–385.Google Scholar
34. Lonchamp, J. F., Barralis, G., Gasquez, J., Jauzein, P., Kerguelen, M., Le Clerch, J., and Maillet, J. 1991. MALHERB, logiciel de reconnaissance des mauvaises herbes des cultures: approache botanique. Weed Res. 31:237–245.Google Scholar
35. Marshall, E.J.P. 1987. Using decision thresholds for the control of the grass and broad-leaved weeds at the Boxworth E.H.F. Proc. Br. Crop Prot. Conf.—Weeds. p. 1059–1066.Google Scholar
36. McKinion, J. M. and Lemmon, H. E. 1985. Expert system for agriculture. Comput. Electron. Agric. 1:31–40.Google Scholar
37. Meriggi, P. and Rosso, F. 1991. Evoluzione del diserbo di post-emergenza con tecnica DMR in bietola e soia. Agronomica 1:8–16.Google Scholar
38. Michalski, R. S., Davis, J. H., Bisht, V. S., and Sinclair, J. B. 1982. PLANT/ds: An expert consulting system for the diagnosis of soybean diseases. p. 133–138 in Proc. 5th Eur. Conf. on Artificial Intell., Orsay, France, July 1982.Google Scholar
39. Mortensen, D. A., Coble, H. D., Smart, J. R., and Bauer, T. A. 1990. Use of an expert system for integrating weed control strategies in a weed science laboratory. J. Agron. Educ. 2:181–183.Google Scholar
40. Muccinelli, M. 1990. Prontuario dei Fitofarmaci. 4th ed. Edagricole Eds. p. 646.Google Scholar
41. Nagarajan, K., Mishoe, J. W., and Currey, W. L. 1987. Development of an expert system for weed management in soybean. Am. Soc. Agric. Eng., no. 87-5024, p. 9.Google Scholar
42. Patterson, D. D. 1985. Comparative ecophysiology of weeds and crops. p. 101–129 in Duke, S. O., ed. Weed Physiology. Vol. I, CRC Press, Boca Raton, FL.Google Scholar
43. Pestemer, W., Gottesburen, B., Wang, K., Wischnewsky, M. B., and Zhao, J. 1990. Possible applications of the expert system HERBASYS (Herbicide Advisory System). Z. Pflanzenkrankheiten und Pflanzenschutz 12:179–190.Google Scholar
44. Pignatti, S. 1982. Flora d'Italia, Edagricole Eds. 3 Vol., p. 780.Google Scholar
45. Pohlmann, J. M. and Reiner, L. 1990. Computer-based weed control with decision support and herbicide selection. Z. Pflanzenkrankheiten und Pflanzenschutz 12:163–169.Google Scholar
46. Rapparini, G. 1989. Il trattamento di post-emergenza reso piu' difficile dall'andamento stagionale. Inf. Agrar. 5:73–89.Google Scholar
47. Resina, C., Vanadia, S., Basile, G., Longo, A., and Stigliani, L. 1989. NOWEED: Sistema Esperto per il controllo delle malerbe. Proc. “7° Simposio Chimica degli Antiparassitari. Agricoltura e Informatica” p. 155–176.Google Scholar
48. Resina, C., Vanadia, S., Basile, G., Longo, A., and Stigliani, L. 1989. Data Base sul diserbo chimico. Proc. “7° Simposio Chimica degli Antiparassitari. Agricoltura e Informatica” p. 141–154.Google Scholar
49. Salonen, J. and Merkkiniemi, R. 1990. Expert system for weed control in cereals as part of an agricultural decision support system in Finland. Proc. Eur. Weed Res. Soc. Symp.: Integrated Weed Management in Cereals, Helsinki. p. 475–477.Google Scholar
50. Scott, R. K. and Wilcockson, S. J. 1976. Weed biology and the growth of sugarbeet. Ann. App. Biol. 83:331–335.Google Scholar
51. Shribbs, J. M., Lybecker, D. W., and Schweizer, E. E. 1990. Bioeconomic weed management models for sugarbeet (Beta vulgaris) production. Weed Sci. 38:436–444.Google Scholar
52. Stigliani, L. and Montemurro, P. 1991. Colture di sostituzione del frumento diserbato con chlorsulfuron, isoproturon, triasulfuron e tribuneron-methyl. Proc. SILM: Il controllo della vegetazione infestante il frumento, Rimini, Italy. p. 263–275.Google Scholar
53. Streibig, J. C., Andreasen, C.H.R., and Fredshavn, J. 1990. A computer programme for economic thresholds. Proc. Eur. Weed Res. Soc. Symp.: Integrated Weed Management in Cereals, Helsinki. p. 459–466.Google Scholar
54. Tilley, L. 1990. Computer based expert system will simplify herbicide choice. BSES Bull. 31:21–22.Google Scholar
55. Tjitrosoedirdjo, S. and Djojomartono, M. 1990. Use of expert system in weed identification and management. BIOTROP Special Publ. 38:257–260.Google Scholar
56. Wiles, L. J., Wilkerson, G. G., and Coble, H. D. 1991. WEEDING: A Weed Ecology and Economic Decision Making INstructional Game. Weed Technol. 5:887–893.Google Scholar
57. Wilson, J. B. and Wright, K. J. 1990. Predicting the growth and competitive effects of annual weeds in wheat. Weed Res. 30:201–211.Google Scholar
58. Zanin, G. and Berti, A. 1991. Possibilita' e limiti dell'impiego delle soglie di infestazione nel mais (Zea mays). Proc. Natl. Conf. Corn, Grado, Italy, p. 181–203.Google Scholar