K. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, Machine learning in agriculture: A review, Sensors, vol.18, issue.8, p.2674, 2018.

S. Dimitriadis and C. Goumopoulos, Applying machine learning to extract new knowledge in precision agriculture applications, Panhellenic Conf. on Inf, pp.100-104, 2008.

R. O. Kuchenbuch and U. Buczko, Re-visiting potassium-and phosphate-fertilizer responses in field experiments and soil-test interpretations by means of data mining, J. of Plant Nutrition and Soil Science, vol.174, issue.2, pp.171-185, 2011.

H. Yu, D. Liu, G. Chen, B. Wan, S. Wang et al., A neural network ensemble method for precision fertilization modeling, Math. and Comp. Modelling, vol.51, issue.11-12, pp.1375-1382, 2010.

A. Shekoofa, Y. Emam, N. Shekoufa, M. Ebrahimi, and E. Ebrahimie, Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: a new avenue in intelligent agriculture, PloS One, vol.9, issue.5, 2014.

J. Yuan, C. Liu, Y. Li, Q. Zeng, and X. F. Zha, Gaussian processes based bivariate control parameters optimization of variable-rate granular fertilizer applicator, Computers and Electronics in Agriculture, vol.70, issue.1, pp.33-41, 2010.

Y. Karimi, S. Prasher, R. Patel, and S. Kim, Application of support vector machine technology for weed and nitrogen stress detection in corn, Computers and electronics in agriculture, vol.51, issue.1-2, pp.99-109, 2006.

J. Gholap, A. Ingole, J. Gohil, S. Gargade, and V. Attar, Soil data analysis using classification techniques and soil attribute prediction, 2012.

S. Jay, F. Maupas, R. Bendoula, and N. Gorretta, Retrieving lai, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and prosail inversion for field phenotyping, Field Crops Research, vol.210, pp.33-46, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01730271

D. M. Haaland and E. V. Thomas, Partial least-squares methods for spectral analyses. 1. relation to other quantitative calibration methods and the extraction of qualitative information, Analytical chemistry, vol.60, issue.11, pp.1193-1202, 1988.

J. Li, W. Huang, L. Chen, S. Fan, B. Zhang et al., Variable selection in visible and near-infrared spectral analysis for noninvasive determination of soluble solids content of 'ya'pear, Food Analytical Methods, vol.7, issue.9, pp.1891-1902, 2014.

P. Guo, Z. Shi, M. Li, W. Luo, and Z. Cha, A robust method to estimate foliar phosphorus of rubber trees with hyperspectral reflectance, Industrial Crops and Products, vol.126, pp.1-12, 2018.

E. Frank and I. H. Witten, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 1999.

A. Savitzky and M. J. Golay, Smoothing and differentiation of data by simplified least squares procedures, Analytical chemistry, vol.36, issue.8, pp.1627-1639, 1964.

H. Martens and E. Stark, Extended multiplicative signal correction and spectral interference subtraction: new preprocessing methods for near infrared spectroscopy, Journal of pharmaceutical and biomedical analysis, vol.9, issue.8, pp.625-635, 1991.

D. H. Douglas and T. K. Peucker, Algorithms for the reduction of the number of points required to represent a digitized line or its caricature, Cartographica: internat. journal for geographic information and geovisualization, vol.10, issue.2, pp.112-122, 1973.