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 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.