A logistic radial basis function regression method for discrimination of cover crops in olive orchards

Expert Systems with Applications

Volumn 37, Issue 12, 2010, Pages 8432-8444

  Hervas Martinez C ^a   Gutierrez P A ^a   Pena Barragan J M ^b   Jurado Exposito M ^b   Lopez Granados F ^b  

^a UNIVERSITY OF CÓRDOBA   (Spain)

^b INSTITUTO DE AGRICULTURA SOSTENIBLE   (Spain)

Author keywords

Cover crops; Evolutionary neural networks; Multiclassification; MultiLogistic regression; Radial basis functions; Spectral signature

Indexed keywords

AGRONOMY; CROPS; ENVIRONMENTAL IMPACT; EVOLUTIONARY ALGORITHMS; FARMS; FORESTRY; FUNCTIONS; HEAT CONDUCTION; IMAGE SEGMENTATION; INFRARED DEVICES; LOCATION; MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATION; NEURAL NETWORKS; OPTIMIZATION; ORCHARDS; RADIAL BASIS FUNCTION NETWORKS; SOILS; TREES (MATHEMATICS);

COVER CROPS; EVOLUTIONARY NEURAL NETWORK; MULTI-CLASSIFICATION; MULTILOGISTIC REGRESSION; RADIAL BASIS FUNCTIONS; SPECTRAL SIGNATURE;

REGRESSION ANALYSIS;

EID: 77957824338     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.05.035     Document Type: Article

References (53)

1. Andalusian Administration Regulation (2007). Andalusian official bulletin (BOJA), 119, 7-37. Available from http://www.juntadeandalucia.es/boja/ boletines/2007/119/d/2.html.
2. Angeline, P. J., Saunders, G. M., & Pollack, J. B. (1994). An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 5, 54-65.
3. Foody, G. M., & Arora, M. K. (1996). Incorporating mixed pixel in the training, allocation and testing stages of supervised classification. Pattern Recognition Letters, 17, 1389-1398.
4. Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In Morgan Kaufmann (Ed.), Proceedings of the thirteenth international conference on machine learning (pp. 148-156).
5. Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings. Annals of Mathematical Statistics, 11, 86-92.
6. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. The Annals of Statistics, 38, 337-374.
7. Fukunaga, K. (1999). Introduction to statistical pattern recognition (2nd ed.). Academic Press.
8. Gill, P. E., Murray, W., & Wright, M. H. (1982). Practical optimization. Academic Press.
9. Goel, P. K., Prasher, S. O., Patel, R. M., Landry, J. A., Bonnel, R. B., & Viau, A. A. (2003). Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Computers and Electronics in Agriculture, 39, 67-93.
10. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, Massachusetts, USA: Addison-Wesley.
11. González-Andujar, J. L. (2009). Expert system for pests, diseases and weeds identification in olive crops. Expert Systems with Applications, 36, 3278-3283.
12. González-Diaz, L., Martínez-Jimenez, P., Bastida, F., & González-Andujar, J. L. (2009). Expert system for integrated plant protection in pepper (Capsicum annuun L). Expert Systems with Applications, 36, 8975-8979.
13. Gutiérrez, P. A., Fernández, J. C., Hervás- Martínez, C., López-Granados, F., Jurado- Expósito, M., & Peña-Barragán, J. M. (2009). Structural simplification for hybrid neuro-logistic regression in multispectral analyse of remote sensed data. Neural Network World, 19, 3-20.
14. Gutiérrez, P. A., López-Granados, F., Peña- Barragán, J. M., Jurado-Expósito, M., Gómez-Casero, M. T., & Hervás-Martínez, C. (2008b). Mapping sunflower yield as affected by Ridolfia segetum patches and elevation by applying Evolutionary Product Unit Neural Networks to remote sensed data. Computers and Electronics in Agriculture, 60, 122-132.
15. Gutiérrez, P. A., López-Granados, F., Peña- Barragán, J. M., Jurado-Expósito, M., & Hervás- Martínez, C. (2008a). Logistic Regression Product Unit Neural Networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data. Computers and Electronics in Agriculture, 64, 293-306.
16. Harvey, K. R., & Hill, G. J. E. (2001). Vegetation mapping of a tropical freshwater swamp in the Northern Territory, Australia: A comparison of aerial photography, Landsat TM and SPOT satellite imagery . International Journal of Remote Sensing, 22, 2911-2925.
17. Hervás-Martínez, C., & Martínez-Estudillo, F. J. (2007). Logistic regression using covariates obtained by product-unit neural network models. Pattern Recognition, 40, 52-64.
18. Hervás-Martínez, C., Martínez-Estudillo, F. J., & Carbonero-Ruz, M. (2008). Multilogistic regression by means of evolutionary product-unit neural networks. Neural Networks, 21, 951-961.
19. Hill, J., Megier, J., & Mehl, W. (1995). Land degradation, soil erosion, and desertification monitoring in Mediterranean ecosystems. International Journal of Remote Sensing, 12, 107-130.
20. Hochberg, Y., & Tamhane, M. (1987). Multiple comparison procedures. John Wiley & Sons.
21. Ibrahim, M. A., Arora, M. K., & Ghosh, S. K. (2005). Estimating and accommodating uncertainty through the soft classification of remote sensing data. International Journal of Remote Sensing, 26, 2995-3007.
22. Karimi, Y., Prasher, S. O., Mcnaim, H., Bonnell, R. B., Dutilleul, P., & Goel, P. K. (2005). Classification accuracy of discriminant analysis, neural networks and decision trees for weed and nitrogen stress detection in corn. Transactions of the American Society of Agricultural Engineerings, 48, 1261-1268.
23. Kin, M. (2009). Two-stage logistic regression model. Expert Systems with Applications, 36, 6727-6734.
24. Koger, C. H., Shaw, D. R., Reddy, K. N., & Bruce, L. M. (2004). Detection of pitted morningglory (Ipomoea lacunosa) with hyperspectral remote sensing. II. Effects of vegetation ground cover and reflectance properties. Weed Science, 52, 230-235.
25. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 1995 international joint conference on artificial intelligence (IJCAI'95) (pp. 1137-1143). Montreal, Quebec, Canadá.
26. Kohavi, R. (1996). Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. In Proceedings of the second international conference on knoledge discovery and data mining (KDD96) (pp. 202-207). Menlo Park, CA: AAAI Press.
27. Landwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine Learning, 59, 161-205.
28. Le Cessie, S., & Van Houwelingen, J. (1992). Ridge estimators in logistic regression. Applied Statistics, 41, 191-201.
29. Lee, C.-C., Chiang, Y.-C., Shih, C.-Y., & Tsai, C.-L. (2009). Noisy time series prediction using M-estimator based robust radial basis function neural networks with growing and pruning techniques. Expert Systems with Applications, 36, 4717-4724.
30. Lee, S., & Kil, R. M. (1991). A Gaussian potential function network with hierarchically selforganizing learning. Neural Networks, 4, 207-224.
31. López-Granados, F., Jurado-Expósito, M., Peña-Barragán, J. M., & García-Torres, L. (2006). Using remote sensing for identification of late season grass weed patches in wheat. Weed Science, 54, 346-353.
32. López-Granados, F., Peña-Barragán, J. M., Jurado-Expósito, M., Francisco-Fernández, M., Cao, R., Alonso-Betanzos, A., et al. (2008). Multispectral classification of grass weeds and wheat (Triticum durum) crop using linear and nonparametric functional discriminant analysis and neural networks. Weed Research, 48, 28-37.
33. MAPYA (2007). Spanish Ministry of Agriculture, Fishery and Food. Available from http://www.mapa.es/es/estadistica/pags/anuario/introduccion.htm.
34. Martínez-Estudillo, A. C., Hervás-Martínez, C., Martínez-Estudillo, F. J., & García- Pedrajas, N. (2006a). Hybridization of evolutionary algorithms and local search by means of a clustering method. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 36, 534-545.
35. Martínez-Estudillo, A. C., Martínez-Estudillo, F. J., Hervás-Martínez, C., & García- Pedrajas, N. (2006b). Evolutionary product unit based neural networks for regression. Neural Networks, 19, 477-486.
36. Moody, J., & Darken, C. J. (1989). Fast learning in network of locally-tuned processing units. Neural Computation, 1, 281-294.
37. Neupane, R. P., Sharma, K. R., & Thapa, G. B. (2002). Adoption of agroforestry in the hills of Nepal: A logistic regression analysis. Agricultural Systems, 72, 177-196.
38. Park, J., & Sandberg, I. W. (1991). Universal approximation using radial basis functions network. Neural Computation, 3, 246-257.
39. Peña-Barragán, J. M., López-Granados, F., Jurado-Expósito, M., & García-Torres, L. (2006). Spectral discrimination of Ridolfia segetum and sunflower as affected by phenological stage. Weed Research, 46, 10-21.
40. Pu, R., & Gong, P. (2004). Determination of burnt scars using logistic regresión and neural network techniques from a single post-fire Landsat-7 ETM + image. Photogrammetric Engineering and Remote Sensing, 70, 841-850.
41. Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
42. Salisbury, J.W. (1999). Spectral measurements field guide. In Report No. ADA362372, 90 (pp. 2-9). Defense Tecnol. Info. Ctr., Fort Bervoi, USA.
43. Schlesinger, W. H. (2000). Carbon sequestration in soils: Some cautions amidst optimism. Agriculture, Ecosystems & Environment, 82, 121-127.
44. Schmidt, K. S., & Skidmore, A. K. (2003). Spectral discrimination of vegetation types in coastal wetland. Remote Sensing of Environment, 85, 92-108.
45. South, S., Qi, J., & Lusch, D. P. (2004). Optimal classification methods for mapping agricultural tillage practises. Remote Sensing of Environment, 91, 90-97.
46. Thenkabail, P. S., Enclona, E. A., Ashton, M. S., & Van Der Meer, B. (2004). Accuracy assessments of hyperspectral wavebands performance of vegetation analysis applications. Remote Sensing of Environment, 91, 354-376.
47. Thenkabail, P. S., Smith, R. B., & De-Pauw, E. (2000). Hyperspectral vegetation indices for determining agricultural crop characteristics. Remote Sensing of Environment, 71, 158-182.
48. Torres, M., Hervás, C., & García, C. (2009). Multinomial logistic regression and product unit neural network models: Application of a new hybrid methodology for solving a classification problem in the livestock sector. Expert Systems with Applications, 36, 12225-12235.
49. Uno, Y., Prasher, S. O., Lacroix, R., Goel, P. K., Karimi, Y., Viau, A., et al. (2005). Artificial Neural Networks to predict corn yield from Compact Airborne Spectrographic Imager data. Computers and Electronics in Agriculture, 47, 149-161.
50. Van Deventer, A. P., Ward, A. D., Gowda, P. H., & Lyon, J. G. (1997). Using Thematic Mapper data to identify contrasting soil plains and tillage practices. Photogrammetric Engineering and Remote Sensing, 63, 87-93.
51. Witten, I.H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. In Morgan Kaufmann (Ed.), Data management systems (2nd ed., ser.). Elsevier.
52. Xu, M., Watanachaturaporn, P., Varshney, P. K., & Arora, M. K. (2005). Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, 97, 322-336.
53. Yao, X., & Liu, Y. (1997). A new evolutionary system for evolving artificial neural networks. IEEE Transactions on Neural Networks, 8, 694-713.