Improved Machine Learning Methodology for High Precision Agriculture

This paper presents the impact of machine learning in precision agriculture. State-of-the-art image recognition is applied to 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 c...

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Bibliographic Details
Published in2018 Global Internet of Things Summit (GIoTS) pp. 1 - 6
Main Authors Treboux, Jerome, Genoud, Dominique
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Agricultural health
Agriculture
Drones
Feature extraction
Hyper-spectral images
Image recognition
Internet of Things
Machine learning
Precision agriculture
Prediction
Roads
Satellites
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Summary:This paper presents the impact of machine learning in precision agriculture. State-of-the-art image recognition is applied to 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 can discriminate interesting objects with an accuracy of (89.6%). The machine learning, an innovative approach for this type of use case, 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.
DOI:10.1109/GIOTS.2018.8534558