Leaf nitrogen determination of corn plant using aerial images and artificial neural networks

Canadian Biosystems Engineering / Le Genie des biosystems au Canada

Volumn 49, Issue , 2007, Pages

  Gautam, R K ^a   Panigrahi, S ^a  

^a North Dakota State University   (United States)

Author keywords

Corn; Image processing; Leaf nitrogen; Neural network; Precision farming

Indexed keywords

CORRELATION METHODS; IMAGE PROCESSING; MEAN SQUARE ERROR; NEURAL NETWORKS; PLANTS (BOTANY);

AERIAL IMAGES; GREEN VEGETATION INDEX (GVI); PRECISION FARMING;

NITROGEN;

EID: 38749120604     PISSN: 14929058     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (61)

1. Al-Abbas, A.H., R. Barr, J.D. Hall, F.L. Crane and M.F. Baumgardner. 1974. Spectra of normal and nutrient-deficient maize leaves. Agronomy Journal 66: 16-20.
2. Atkinson, P.M. and R.L. Tatnall. 1997. Neural networks in remote sensing: Introduction. International Journal of Remote Sensing 18 (4): 699-709.
3. Baldwin, A.H., K.L. McKee and I.A. Mendelssohn. 1996. The influence of vegetation, salinity, and inundation on seed banks of oligohaline coastal marshes. American Journal of Botany 83 (4): 470-479.
4. Beatty M.K., R. Caldanaro, C.J. Johanssen and K. Ross. 2000. In situ detection of leaf chlorophyll content and leaf nitrogen content in Zea Mays L. using remote sensing. Proceedings of the Fifth International Conference on Precision Agriculture (on CD). Madison, WI: American Society of Agronomy.
5. Behloul, F., B.P.F. Lelieveldt, A. Boudraa and J.H.C. Reiber. 2002. Optimal design of radial basis function neural networks for fuzzy-rule extraction in high dimensional data. Pattern Recognition 35: 659-675.
6. Benediktsson, J.A., P.H. Swain and O.K. Ersoy. 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing 28: 540-552.
7. Bettinger, P., G.A. Bradshaw and G.W. Weaver. 1996. Effects of geographic information system vector-raster-vector data conversion on landscape indices. Canadian Journal of Forest Research 26 (8): 1416-1425.
8. Bishop, C.M. 1995. Neural Networks for Pattern Recognition. Oxford, England: Clarendon Press.
9. Black, C.A. 1999. Soil Fertility Evaluation and Control. Boca Raton, FL: Lewis Publishers.
10. Blackmer A. and S.E. White. 1998. Using precision farming technologies to improve management of soil and fertilizer nitrogen. Australian Journal of Agricultural Resource 49: 555-564.
11. Blackmer, T.M., J.S. Shepers, G.S. Varvel and E. Water-Shea. 1996. Nitrogen deficiency detection using reflected short wave radiation from irrigated corn canopies. Agronomy Journal 88: 1-5.
12. Bogdanov, A.V., S. Sandven, O.M. Johannessen, V.Y. Alexandrov and L.P. Bobylev. 2005. Multisensor approach to automated classification of sea ice image data. IEEE Transactions on Geoscience and Remote Sensing 43 (7): 1648-1664.
13. Brown, M. and C.J. Harris. 1994. Neurofuzzy Adaptive Modeling and Control. New York, NY: Prentice Hall.
14. Chen, S., C.F.N. Cowan and P.M. Grant. 1991. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2(2): 302-309.
15. Cichocki, A. and R. Unbehauen. 1993. Neural Networks for Optimization and Signal Processing. Chichester, NY: John Wiley & Sons.
16. Cihacek, L.J. and T.A. Kerby. 1991. Effects of residual soil nitrogen and urea on yield and petiole nitrate of cotton. Journal of Production Agriculture 4:193-197.
17. Critten, D.L. 1997. Fractal dimension relationships and values associated with certain plant canopies. Journal of Agricultural Engineering Research 67 (1): 61-72.
18. Davis, A.M.C. 1998. Cross validation: Do we love it too much? Spectroscopy Europe 10 (2): 24-25.
19. Diker, K., D.F. Heermann, W.C. Bausch and D.K. Wright. 2004. Shannon-Wiener's diversity index for linking yield monitor and remotely sensed data for corn. Transactions of the ASAE 47(4): 1347-1354.
20. Drummond, S.T., K.A. Sudduth, A. Joshi, S.J. Birrell and N.R. Kitchen. 2002. Statistical and neural methods for site-specific yield prediction. Transactions of the ASAE 47 (1): 5-14.
21. Fernandez, A., P. Illera and J.L. Casanova. 1997. Automatic mapping of surfaces affected by forest fires in Spain using AVHRR NDVI composite image data. Remote Sensing Environment 60 (2): 153-162.
22. Filella, I., L. Serrano, J. Serra and J. Penuelas. 1994. Crop ecology production and management: Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science 35: 1400-1405.
23. Flowers, M., R. Weisz and R. Heiniger. 2000. Aerial photographic determination of. nitrogen application timing and rate recommendations in winter wheat. Proceedings of the Fifth International Conference on Precision Agriculture (on CD). Madison, WI: American Society of Agronomy.
24. Foody, G.M. 1995a. Using prior knowledge in artificial neural network classification with a minimal training set. International Journal of Remote Sensing 16: 301-312.
25. Foody, G.M. 1995b. Land cover classification using an artificial neural network with ancillary information. International Journal of Geographical Information Systems 9: 527-542.
26. Gaston, G.G., P.M. Bradley, T.S. Vinson and T.P. Kolchugina. 1997. Forest ecosystem modeling in the Russian Far East using vegetation and land-cover regions identified by classification of GVI. Photogrammetry Engineering and Remote Sensing 63 (1): 51-58.
27. Goodin, D.G. and G.M. Henebry. 1997. A technique for monitoring ecological disturbance in tallgrass prairie using seasonal NDVI trajectories and a discriminant function mixture model. Remote Sensing Environment 61 (2): 270-278.
28. Greenwood, D.J., F. Gastal, G. Lemaire, A. Draycott, P. Millard and J.J. Neeteson. 1991. Growth rate and % N of field grown crops: Theory and experiments. Annals of Botany 67: 181-190.
29. Haralick, R.M., K. Shanmugam and I. Dinstein. 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernatics 3 (6): 610-621.
30. Hassoun, M.H. 1995. Fundamentals of Artificial Neural Networks. Cambridge, MA: MIT Press.
31. st edition. New Jersey, NJ: Prentice Hall Inc.
32. Honavar, V. and L. Uhr. 1994. Artificial Intelligence and Neural Networks: Steps towards Principled Integration. Boston, MA: Academic Press.
33. Hong, N.J., S. Rim, J. Lee and J. Kim. 1997. Remote sensing for estimating chlorophyll amount in rice canopies. Proceedings of 1997 IEEE International Geoscience and Remote Sensing Symposium 1: 89-91.
34. Huang, D. S. 1999. Radial basis probabilistic neural networks: Model and application. International Journal of Pattern Recognition and Artificial Intelligence 13(7): 1083-1101.
35. Huemmrich, K.R. and S.N. Goward. 1997. Vegetation canopy PAR absorptance and NDVI: An assessment for ten tree species with the SAIL model. Remote Sensing Environment 61(2): 254-269.
36. Huggins, D.R. and W.L. Pan. 1993. Nitrogen efficiency component analysis: An evaluation of cropping system differences in productivity. Agronomy Journal 85:898-905.
37. Idrisi32.11. 2000. Guide to GIS and Image Processing, volumes 1-2. Worcester, MA: Clark Labs.
38. Lecain, D.R., J.A. Morgan, G.E. Schuman, J.D. Reeder and R.H. Hart. 2002. Carbon exchange and species composition of grazed pastures and exclosures in the shortgrass steppe of Colorado. Agricultural Ecosystem and Environment 93 (1/3): 421-435.
39. Majumdar, S. and D.S. Jayas. 1999. Classification of bulk samples of cereal grains using machine vision. Journal of Agricultural Engineering Research 73: 35-47.
40. Monmonier, M.S. 1974. Measures of pattern complexity for cholopleth maps. The American Cartographer 1: 159-169.
41. NeuralWare. 2000. Professional II/Plus tool kit, Reference Manual. Carnegie, PA: NeuralWare.
42. O'Neal, M.R., B.A. Engel, D.R. Ess and J.R. Frankenberger. 2002. Neural network prediction of maize yield using alternative data coding algorithm. Biosystems Engineering 83(1): 31-45.
43. Pachepsky, V., A.D. Timlin and G.Varallyay. 1996. Artificial neural networks to estimate soil water retention from easily measurable data. Soil Science Society of America Journal 60(3): 727-733.
44. Pal, N.R., S. Pal, J. Das and K. Majumdar. 2003. SOFM-MLP: A hybrid neural network for atmospheric temperature prediction. IEEE Transactions on Geoscience and Remote Sensing 41(12): 2783-2791.
45. Plate, T., J. Bert, J. Grace and P. Band. 2000. Visualizing the function computed by a feed forward neural network. Neural Computation 12(6): 1355-1370.
46. Podnar, D., D. Koracin and A. Panorska. 2002. Application of artificial neural networks to modeling the transport and dispersion of tracers in complex terrain. Atmospheric Environment 36: 561-570.
47. Reeves, D.W., P.L. Mask, C.W. Wood and D.P. Delaney. 1993. Determination of wheat nitrogen status with a hand held chlorophyll meter: Influence of management practices. Journal of Plant Nutrition 16(5): 781-796.
48. Sauchelli, V. 1964. Fertilizer Nitrogen: Its Chemistry and Technology. New York, NY: Reinhold Publishing Corp.
49. Schalkoff, R. 1992. Pattern Recognition: Statistical, Structural, and Neural Approaches. New York, NY: Wiley Publishing.
50. Scharf, P.C. and J.A. Lory. 2000. Calibration of remotely sensed corn color to predict nitrogen need. Proceedings of the Fifth International Conference on Precision Agriculture (on CD). Madison, WI: American Society of Agronomy.
51. Sérélé, C.Z., Q.H.J. Gwyn, J. Boisvert, E. Pattey, N. McLaughlin and G. Daoust. 2000. Corn yield prediction with artificial neural network trained using airborne remote sensing and topographic data. Proceedings of 2000 IEEE International Geoscience and Remote Sensing Symposium 1: 384-386.
52. Shackleton, C. 2002. Nearest-neighour analysis and the prevelance of woody plant competition in South African savannas. Plant Ecology 158 (1): 65-76.
53. Sheftel, B.I. and I. Hanski. 2002. Species richness, relative abundances and habitat use in local assemblages of Sorex shrews in Eurasian boreal forests. Acta Theriologica 47 (1): 69-79.
54. Spitz, F. and S. Lek. 1999. Environmental impact prediction using neural network modeling: An example in wildlife damage. Journal of Applied Ecology 36: 317-326.
55. Stone, M.L., J.B. Solie, W.R. Raun, R.W. Winey, S.L. Taylor and J.D. Ringer. 1996. Use of spectral radiance for correcting in-season fertilizer nitrogen deficiencies in winter wheat. Transaction of the ASAE 39 (5): 1623-1631.
56. Thai, C.N. and R.L. Shewfelt. 1991. Modeling sensory color quality of tomato and peach: Neural networks and statistical regression. Transactions of the ASAE 34(3): 950-955.
57. Tumbo, S.D., D.G. Wagner and P.H. Heinemann. 2002. Hyperspectral-based neural network for predicting chlorophyll status in corn. Transactions of the ASAE 45 (3): 825-832.
58. Turner, M.G. 1989. Landscape ecology: The effect of pattern on process. Annual Revision of Ecological Systems 20: 171-197.
59. Viets, F.G and R.H. Hageman. 1971. Factors affecting the accumulation of nitrate in soil, water, and plants. Agriculture Handbook No. 413. Washington, DC: Agricultural Research Service, USDA.
60. Yang, C.C., S.O. Prasher and R. Lacroix. 1996. Applications of artificial neural networks to land drainage engineering. Transactions of the ASAE 39(2): 525-533.
61. Zhuang, X. and A.E. Bernard. 1990. Classification of multispectral remote sensing data using neural network vs statistical methods. ASAE Paper No. 90-7552. St Joseph, MI: ASABE.