Development of a herbicide application map using artificial neural networks and fuzzy logic

Agricultural Systems

Volumn 76, Issue 2, 2003, Pages 561-574

  Yang, Chun Chieh ^a   Prasher, Shiv O ^a   Landry, Jacques Andre ^a   Ramaswamy, Hosahalli S ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

Artificial neural network; Fuzzy logic; Greenness; Herbicide application; Image processing; Precision agriculture; Site specific farming; Weed map

Indexed keywords

ARTIFICIAL NEURAL NETWORK; FUZZY MATHEMATICS; HERBICIDE; IMAGE PROCESSING; MAPPING;

ZEA MAYS;

EID: 0037400720     PISSN: 0308521X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0308-521X(01)00106-8     Document Type: Article

References (43)

1. Ascheman, R.E., 1993. Some practical field applications. In: Soil Specific Crop Management. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, USA, pp. 79-86.
2. Bárdossy, A., Duckstein, L., 1995. Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological and Engineering Systems. CRC Press, Boca Raton, Florida.
3. Biller, R.H., Hollstein, A., Sommer, C., 1997. Precision application of herbicides by use of optoelectronic sensors. In: Stafford, J.V. (Ed.), Precision Agriculture '97, Vol. II, 1997. (Ed.), Technology, IT and Management. SCI Bios Scientific Publishers, Oxford, USA, pp. 451-458.
4. Blackmer, T.M., Schepers, J.S., 1996. Using DGPS to improve corn production and water quality. GPS World 7 (3), 44-52.
5. Blackmore, S., 1994. Precision farming: an introduction. Outlook on Agriculture 23 (4), 275-280.
6. Center, B., Verma, B.P., 1997. A fuzzy photosynthesis model for tomato. Transactions of the ASAE 40 (3), 815-821.
7. Colvin, T.S., Jaynes, D.B., Karlen, D.L., Laird, D.A., Ambuel, J.R., 1997. Yield variability within a central Iowa field. Transactions of the ASAE 40 (4), 883-889.
8. Deck, S.H., Morrow, C.T., Heinemann, P.H., Sommer III, H.J., 1995. Comparison of a neural network and traditional classifier for machine vision inspection of potatoes. Applied Engineering in Agriculture 11 (2), 319-326.
9. Engqvist, A., Bengtsson, P., Enfalt, P., 1997. A model for site specific broad-leaved weed control based on weed plant variables. In: Stafford, J.V. (Ed.), Precision Agriculture '97, Vol. II, 1997. (Ed.), Technology, IT and Management. SCI Bios Scientific Publishers, Oxford, USA, pp. 869-878.
10. Ghazanfari, A., Wulfsohn, D., Irudayaraj, J., 1998. Machine vision grading of pistachio nuts using Gray-level histogram. Canadian Agricultural Engineering 40 (1), 61-66.
11. Green, H.M., Vencill, W.K., Kvien, C.K., Boydell, B.C., Pocknee, S., 1997. Precision management of spatially variable weeds. In: Stafford, J.V. (Ed.), Precision Agriculture '97, Vol. II, 1997. (Ed.), Technology, IT and Management. SCI Bios Scientific Publishers, Oxford, USA, pp. 983-989.
12. Hartzler, R.G., 1997. Velvetleaf (Abutilon theophrasti) interference in soybean (Glycine max): a survey of yield loss estimates and management recommendations. Crop Protection 16 (5), 483-485.
13. Heske, T., Heske, J.N., 1996. Fuzzy Logic for Real World Design. Annabooks, San Diego, California.
14. Kasabov, N.K., 1996. Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. The MIT Press, Cambridge, Massachusetts.
15. Kim, S., Cho, S.I., 1997. Neural network modeling and fuzzy control simulation for bread-baking process. Transactions of the ASAE 40 (3), 671-676.
16. Kollias, V.J., Kalivas, D.P., 1998. The enhancement of a commercial geographical information system (ARC/INFO) with fuzzy processing capabilities for the evaluation of land resources. Computers and Electronics in Agriculture 20 (1), 79-95.
17. Kulkarni, A.D., 1994. Artificial Neural Networks for Image Understanding. Van Nostrand Reinhold, New York.
18. Lacroix, R., Huijbers, J., Tiemessen, R., Lefebvre, D., Marchand, D., Wade, K.M., 1998. Fuzzy set-based analytical tools for dairy herd improvement. Applied Engineering in Agriculture 14 (1), 79-85.
19. Luo, X., Jayas, D.S., Symons, S.J., 1999. Comparison of statistical and neural network methods for classifying cereal grains using machine vision. Transactions of the ASAE 42 (2), 413-419.
20. Mannion, A.M., 1995. Agriculture and Environmental Change: Temporal and Spatial Dimensions. John Wiley, New York.
21. MathWorks, 1998a. Image Processing Toolbox User's Guide, for Use with MATLAB. The MathWorks, Inc, Natick, Massachusetts.
22. MathWorks, 1998b. Fuzzy Logic Toolbox User's Guide, for Use with MATLAB. The MathWorks, Inc, Natick, Massachusetts.
23. MathWorks, 1999. Using MATLAB. The MathWorks, Inc, Natick, Massachusetts.
24. Nakano, K., 1997. Application of neural networks to the color grading of apples. Computers and Electronics in Agriculture 18 (2-3), 105-116.
25. NeuralWare, 1995. NeuralWorks Professional II/PLUS v 5.2 and NeuralWorks Explorer v 5.2. Neural-Ware, Inc, Pittsburgh, Pennsylvania.
26. Ngouajio, M., Lemieux, C., Fortier, J.-J., Careau, D., Leroux, G.D., 1998. Validation of an operator-assisted module to measure weed and crop leaf cover by digital image analysis. Weed Technology 12 (3), 446-453.
27. Paice, M.E.R., Miller, P.C.H., Day, W., 1996. Control requirements for spatially selective herbicide sprayers. Computers and Electronics in Agriculture 14 (2-3), 163-177.
28. Pedrycz, W., 1991. Application of fuzzy logic in control systems. In: Automated Agriculture for the 21st Century, Proceedings of the 1991 Symposium, pp. 209-217.
29. Peralta, R.C., Hegazy, M.A., Musharrafieh, G.R., 1994. Preventing pesticide contamination of ground-water while maximizing irrigated crop yield. Water Resources Research 30 (11), 3183-3193.
30. Perret, J.S., Prasher, S.O., 1998. Applications of fuzzy logic in the design of vegetated waterways under uncertainty. Journal of the American Water Resources Association 34 (6), 1355-1367.
31. Petersen, G.W., Bell, J.C., McSweeney, K., Nielsen, G.A., Robert, P.C., 1995. Geographic information systems in Agronomy. Advances in Agronomy 55, 67-111.
32. Sayeed, M.S., Whittaker, A.D., Kehtarnavaz, N.D., 1995. Snack quality evaluation method based on image features and neural network prediction. Transactions of the ASAE 38 (4), 1239-1245.
33. Schmoldt, D.L., Li, P., Abbott, A.L., 1997. Machine vision using artificial neural networks with local 3d neighbourhoods. Computers and Electronics in Agriculture 16 (3), 255-271.
34. Scholes, C., Clay, S.A., Brix-Davis, K., 1995. Velvetleaf (Abutilon theophrasti) effect on corn (Zea Mays) growth and tield in south Dakota. Weed Technology 9 (4), 665-668.
35. Smith Jr., S., Schreiber, J.D., Cullum, R.F., 1995. Upland soybean production: surface and shallow groundwater quality as affected by tillage and herbicide use. Transactions of the ASAE 38 (4), 1061-1068.
36. Timmermans, A.J.M., Hulzebosch, A.A., 1996. Computer vision system for on-line sorting of pot plants using an artificial neural network classifier. Computers and Electronics in Agriculture 15 (1), 41-55.
37. Vangessel, M.J., Schweizer, E.E., Garrett, K.A., Westra, P., 1995. Influence of weed density and distribution on corn (Zea mays) yield. Weed Science 43 (2), 215-218.
38. Wallace, T., 1999. P, Small plot evaluation of an electro-optical cotton yield monitor. Computers and Electronics in Agriculture 23 (1), 1-8.
39. Walter, A.M., Heisel, T., Christensen, S., 1997. Shortcuts in weed mapping. In: Stafford, J.V. (Ed.), Precision Agriculture '97, Vol. II, 1997. (Ed.), Technology, IT and Management. SCI Bios Scientific Publishers, Oxford, USA, pp. 777-784.
40. Wang, D., Dowell, F.E., Lacey, R.E., 1999. Single wheat kernel color classification using neural networks. Transactions of the ASAE 42 (1), 233-240.
41. Whitney, J.D., Miller, W.M., Wheaton, T.A., Salyani, M., Schueller, J.K., 1999. Precision farming applications in Florida citrus. Applied Engineering in Agriculture 15 (5), 399-403.
42. Yang, C-C., Prasher, S.O., Landry, J-A., Ramaswamy, H.S., DiTommaso, A., 2000a. Application of artificial neural networks in image recognition and classification of crop and weeds. Canadian Agricultural Engineering 42 (3), 147-152.
43. Yang, C.-C., Prasher, S.O., Landry, J.-A., Perret, J., Ramaswamy, H.S., 2000b. Recognition of weeds with image processing and their use with fuzzy logic for precision farming. Canadian Agricultural Engineering 42 (4), 195-200.