Genetic programming for agricultural purposes

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.

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