ARCHIVES

[1] S. Wolfert, L. Ge, C. Verdouw and M.-J. Bogaardt, “Big data in smart farming – a review,” Agricultural Systems, vol. 153, pp. 69–80, 2017.
[2] W. Klümper and M. Qaim, “A meta-analysis of the impacts of genetically modified crops,” PLoS ONE, vol. 9, no. 11, e111629, 2014.
[3] M. Qaim and D. Zilberman, “Yield effects of genetically modified crops in developing countries,” Science, vol. 299, no. 5608, pp. 900–902, 2003.
[4] J. K. Vessey, “Plant growth promoting rhizobacteria as biofertilizers,” Plant and Soil, vol. 255, no. 2, pp. 571–586, 2003.
[5] P. N. Bhattacharyya and D. K. Jha, “Plant growth-promoting rhizobacteria (PGPR): emergence in agriculture,” World Journal of Microbiology and Biotechnology, vol. 28, no. 4, pp. 1327–1350, 2012.
[6] K. G. Liakos, P. Busato, D. Moshou, S. Pearson and D. Bochtis, “Machine learning in agriculture: a review,” Sensors, vol. 18, no. 8, p. 2674, 2018.
[7] A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: a survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, 2018.
[8] T. van Klompenburg, A. Kassahun and C. Catal, “Crop yield prediction using machine learning: a systematic literature review,” Computers and Electronics in Agriculture, vol. 177, p. 105709, 2020.
[9] A. Chlingaryan, S. Sukkarieh and B. Whelan, “Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review,” Computers and Electronics in Agriculture, vol. 151, pp. 61–69, 2018.
[10] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
[11] J. H. Jeong et al., “Random forests for global and regional crop yield predictions,” PLoS ONE, vol. 11, no. 6, e0156571, 2016.
[12] I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003.
[13] R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1–2, pp. 273–324, 1997.
[14] J. H. Holland, Adaptation in Natural and Artificial Systems. Ann Arbor, MI: University of Michigan Press, 1975.
[15] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1989.
[16] R. Leardi, R. Boggia and M. Terrile, “Genetic algorithms as a strategy for feature selection,” Journal of Chemometrics, vol. 6, no. 5, pp. 267–281, 1992.
[17] F. Pedregosa et al., “Scikit-learn: machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
[18] J. Cohen, Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates, 1988.