Genetic programming for agricultural purposes
Pages 783 - 790
Abstract
Nitrogen is one of the most important chemical intakes to ensure the healthy growth of agricultural crops. However, some environmental concerns emerge (soil and water pollution) when a farmer applies nitrogen in excess. In this study, we propose a new method called GP-SVI to search for the best descriptive model of nitrogen content in a cornfield (Zea mays), thanks to airborne hyperspectral data and ground truth nitrogen measurements. Coupling the output of this descriptive model with variable-rate technologies (VRT) would allow farmers to practice site-specific management ensuring them economical savings and ecological benefits. GP-SVI is a parallel search of the best spectral vegetation index (SVI) describing a crop biophysical variable, derived from Genetic Programming (GP). Compared to statistical regression methods on our datasets, GP-SVI improves results obtained with classical approaches, in term of explained-variance and generalization error. We also show that the spectral bands selected by GP-SVI match those selected by Partial Least Square regression optimized by Genetic Algorithms (GA-PLS) as proposed by Leardi in "Application of genetic algorithm-PLS for feature extraction in spectral data sets", in Journal of Chemometrics.
References
[1]
Bellman, R. Adaptive Control Processes: A Guided Tour, 1961.
[2]
Chen, K. S., Tzeng, Y. C., Chen, C. F., Kao, W. L. and Ni, C. L., Classification of multispectral imagery using dynamic learning neural network. in Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium, 18-21 Aug. 1993, (Tokyo, Japan, 1993), IEEE, 896--898.
[3]
Curran, P. J., Kupiec, J. A. and Smith, G. M. Remote Sensing the Biochemical Composition of a Slash Pine Canopy. IEEE Transactions on Geoscience and Remote Sensing, 35 (2). 415--420.
[4]
Dubrovin, V. I., Subbotin, S. A., Morshchavka, S. V. and Piza, D. M., The plant recognition on remote sensing results by the feed-forward neural networks. in ANNIE 2000. Smart Engineering Systems Design Conference, 5-8 Nov. 2000, (St. Louis, MO, USA, 2000), ASME, 697--702.
[5]
Dwyer, L. M., Anderson, A. M., Ma, B. L., Stewart, D. W., Tollenaar, M. and Gregorich, E. Quantifying the nonlinearity in chlorophyll meter response to corn leaf nitrogen concentration. Canadian Journal of Plant Science, 75 (1). 179--182.
[6]
Goel, P. K., Prasher, S. O., Landry, J. A., Patel, R. M., Bonnel, R. B., Viau, A. A. and Miller, J. R. Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and electronics in agriculture, 38. 99--124.
[7]
Goel, P. K., Prasher, S. O., Patel, R. M., Landry, J. A., Bonnell, R. B. and Viau, A. A. 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 (2). 67--93.
[8]
Hansen, P. M. and Schjoerring, J. K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86. 542--553.
[9]
Jacquemoud, S. Utilisation de la haute résolution spectrale pour l'étude des couverts végétaux: développement d'un modèle de réflectance spectrale Méthodes Physiques en Télédétection, Paris VII, Paris, 1992, 92.
[10]
Koza, J. R. Genetic Programming-On the programming of Computers by Means of Natural Selection. MIT Press, Cambridge, London, 1992.
[11]
Kumar, S., Ghosh, J., Crawford, M. M. and Member, I. Best-Bases Feature Extraction Algorithms for Classification of Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 39 (NO.7). 1368--1379.
[12]
Leardi, R. Application of genetic algorithm-PLS for feature selection in spectral data sets. Journal of Chemometrics, 14. 643--655.
[13]
Moran, M. S., Inoue, Y. and Barnes, E. M. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61 (3). 319--346.
[14]
Peñuelas, J. and Filella, I. Technical Focus: Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in plant science, 3 (1). 151--156.
[15]
Peñuelas, J., Piñol, J., Ogayar, R. and Fillela, I. Estimation of plant water content by the reflectance Water Index WI (R900/R970). International Journal of Remote Sensing, 18 (13). 2869--2875.
[16]
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. and Sorooshian, S. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48 (2). 119--126.
[17]
Shock, B. M., Carpenter, G. A., Gopal, S. and Woodcock, C. E., ARTMAP neural network classification of land use change. in Proceedings of the World Congress of Computers in Agriculture and Natural Resources, 19-21 Sept. 2001, (Iguacu Falls, Brazil, 2001), American Soc. Agric. Eng, 22--28.
[18]
Strachan, I. B., Pattey, E. and Boisvert, J. B. Impact of nitrogen environmental conditions on corn as detected by hyperspectral reflectance. Remote Sensing of Environment, 80 (2). 213--224.
[19]
Xiang, Z., Suhong, L. and Zhenkun, T., A Method for estimating Chlorophyll Content of Wheat from Reflectance Spectra. in Geoscience and Remote Sensing Symposium, IGARSS'04, (2004), 4504--4507.
[20]
Yao, H. and Tian, L. A genetic-algorithm-based selective principal component analysis (GA-SPCA) method for high-dimensional data feature extraction. IEEE Transactions on Geoscience and Remote Sensing, 41 (6 PART I). 1469--1478.
Index Terms
- Genetic programming for agricultural purposes
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Published: 08 July 2006
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