%Aigaion2 BibTeX export from HES SO Valais Publications
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@INPROCEEDINGS{,
     author = {Treboux, Jerome and Genoud, Dominique},
   keywords = {Agricultural health, Hyper-spectral images, Image recognition, machine learning, Precision agriculture, Prediction},
      month = may,
      title = {Improved Machine Learning Methodology for High Precision Agriculture},
       year = {2018},
  publisher = {Inproceedings of the 2018 Global Internet of Things Summit (GIoTS)},
   location = {Bilbao, Spain},
   abstract = {This paper presents the impact of machine learning in precision agriculture. State-of-the art image recognition is applied on a dataset composed of high precision aerial pictures of vineyards. The study presents a comparison of an innovative machine learning methodology compared to a baseline used classically on vineyard and agricultural objects. The baseline uses color analysis and is able discriminates interesting objects with an accuracy of 89.6 \%. The machine learning innovative approach demonstrates that the results can be improved to obtain 94.27 \% of accuracy. Machine Learning used to enrich and improve the detection of precise agricultural objects is also discussed in this study and opens new perspectives for the future of high precision agriculture.}
}