High Precision Agriculture: An Application Of Improved Machine-Learning Algorithms
| Tipo de publicação: | Inproceedings |
| Citação: | |
| Ano: | 2019 |
| Páginas: | 103-108 |
| Publisher: | IEEE 2019 6th Swiss Conference on Data Science (SDS) |
| Location: | Bern, Switzerland |
| URL: | http://ieeexplore.ieee.org/sta... |
| DOI: | 10.1109/SDS.2019.00007 |
| Resumo: | This paper presents the performances of machine learning algorithms on aerial images object detection for high precision agriculture. The dataset used focuses on geotagged pictures of vineyards. We demonstrate that advanced machine learning methodologies like Decision Tree Ensemble, outperform state-of-the-art image recognition algorithms generally used within the agriculture field. The innovative approach described here improve object detection and obtain an accuracy of 94.27% which is an increase of more than 4% compared to the state-of-the-art. Finally, methodology and possible developments for high precision agriculture is discussed in this study. |
| Palavras-chave: | advanced machine learning methodologies, aerial images, Agricultural health, agriculture, dataset, decision tree ensemble, decision trees, feature extraction, high precision agriculture, Hyper-spectral images, Image color analysis, Image recognition, image recognition algorithms, improved machine-learning algorithms, learning (artificial intelligence), machine learning, object detection, Precision agriculture, Prediction, Random Forests, Roads |
| Autores | |
| Adicionado por: | [] |
| Total mark: | 0 |
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