NEPER-weed: A picture-based expert system for weed identification

Agronomy Journal

Volumn 88, Issue 3, 1996, Pages 423-427

  Schulthess, Urs ^a   Schroeder, Kris ^d   Kamel, Ahmed ^d   AbdElGhani, AbdElGhani M ^b   Hassanein, El Hassanein E ^b   AbdElHady, Shaban Sh ^c   AbdElShafi, AbdElMaboud ^b   Ritchie, Joe T ^a   Ward, Richard W ^a   Sticklen, Jon ^d  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b PLANT PATHOLOGY RESEARCH INSTITUTE   (Egypt)

^c CAIRO UNIVERSITY   (Egypt)

^d Michigan State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

POACEAE;

EID: 10144224907     PISSN: 00021962     EISSN: None     Source Type: Journal    
DOI: 10.2134/agronj1996.00021962008800030010x     Document Type: Article

References (20)

1. Atkinson, W.D., and A. Gammerman. 1987 An application of expert system technology to biological identification. Taxon 36:705-714.
2. Behrendt, S., and M. Hanf. 1979. Grass weeds in world agriculture. BASF, Ludwigshafen, Germany.
3. Chancellor, R. J. 1966. The identification of weed seedlings of farm and garden. Blackwell Scientific, Oxford.
4. Chandrasekaran, B. 1986. Generic tasks in knowledge-based reasoning: High-level building blocks for expert system design. IEEE Expert 1(3):23-30.
5. Edwards-Jones, G. 1992. Knowledge-based systems for pest management: An applications-based review. Pestic. Sci. 36:143-153.
6. Fermanian, T.W., and R.S. Michalski. 1989. Weeder: An advisory system for the identification of grasses in turf. Agron. J. 81:312-316.
7. Fermanian, T.W., R.S. Michalski, B. Katz, and J. Kelly. 1989. Agassistant: An artificial intelligence system for discovering patterns in agricultural knowledge and creating diagnostic advisory systems. Agron. J. 81:306-312.
8. Hanf, M. 1983. The arable weeds of Europe with their seedlings and seeds. 1st ed. BASF United Kingdom Ltd. Hadleigh, Suffolk, England.
9. Hanf, M. 1990. Ackerunkräuter Europas: The arable weeds of Europe with their seedlings and seeds. (In German.) 3rd ed. BLV-Verlagsgesellschaft, Munich, Germany.
10. Jones, S.B., and A.E. Luchsinger. 1986. Plant systematics. 2nd ed. McGraw-Hill, New York.
11. Kamel, A., J. McDowell, and J. Sticklen. 1994. Multiple design: An extension of routine design for generating multiple design alternatives. p. 275-292. In J.S. Gero (ed.) Artificial intelligence in design '94. Kluwer Academic Publ., Lausanne, Switzerland.
12. Kamel, A., K. Schroeder, J. Sticklen, A. Rafea, A. Salah, U. Schulthess, R. Ward, and J. Ritchie. 1995. An integrated wheat crop management system based on generic task knowledge based systems and CERES numerical simulation. AI Applic. 9:17-28.
13. Koch, W., and M. Hess. 1980. Weeds in wheat. p. 33-40. In E. Häfliger (ed.) Wheat documenta Ciba-Geigy. Ciba-Geigy, Basel, Switzerland.
14. Longchamp, J.-P., G. Barralis, J. Gasquez, P. Jauzein, M. Kerguelen, J. Le Clerch, and L. Maillet. 1991. Malherb, logiciel de reconnaisance des mauvaises herbes des cultures: approche botanique. Weed Res. 31:237-245.
15. Morse, L.E. 1971. Specimen identification and key construction with time-sharing computers. Taxon 20:269-282.
16. Pankhurst, R.J. 1991. Practical taxonomic computing. Cambridge Univ. Press, Cambridge.
17. Pasqual, G. M. 1994. Development of an expert system for the identification and control of weeds in wheat, triticale, barley and oat crops. Comput. Electron. Agric. 10:117-134.
18. Sticklen, J., and B. Chandrasekaran. 1989a. Distributed causal reasoning. Knowl. Acquis. 1:139-162.
19. Sticklen, J., and B. Chandrasekaran. 1989b. Integrating classification-based compiled level reasoning with function-based deep level reasoning. p. 191-220. In W. Horn (ed.) Causal AI models: Steps towards applications. Hemisphere Publishing, New York.
20. Stucky, J.M. 1980. Identifying seedling and mature weeds common in the Southeastern United States. N.C. Agric. Ext. Serv., Raleigh, NC.