Improved Machine Learning Methodology for High Precision Agriculture
| Publicatietype: | In proceedings |
| Citatie: | |
| Jaar: | 2018 |
| Maand: | Mei |
| Uitgever: | Inproceedings of the 2018 Global Internet of Things Summit (GIoTS) |
| Locatie: | Bilbao, Spain |
| Samenvatting: | 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. |
| Trefwoorden: | Agricultural health, Hyper-spectral images, Image recognition, machine learning, Precision agriculture, Prediction |
| Auteurs | |
| Toegevoegd door: | [] |
| Totaalscore: | 0 |
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