Precision Agriculture: Crop Mapping using Machine Learning and Sentinel-2 Satellite Imagery
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916160068386816 |
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| author | Zhao, Kui Wu, Siyang Liu, Chang Wu, Yue Efremova, Natalia |
| author_facet | Zhao, Kui Wu, Siyang Liu, Chang Wu, Yue Efremova, Natalia |
| contents | Food security has grown in significance due to the changing climate and its warming effects. To support the rising demand for agricultural products and to minimize the negative impact of climate change and mass cultivation, precision agriculture has become increasingly important for crop cultivation. This study employs deep learning and pixel-based machine learning methods to accurately segment lavender fields for precision agriculture, utilizing various spectral band combinations extracted from Sentinel-2 satellite imagery. Our fine-tuned final model, a U-Net architecture, can achieve a Dice coefficient of 0.8324. Additionally, our investigation highlights the unexpected efficacy of the pixel-based method and the RGB spectral band combination in this task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09651 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Precision Agriculture: Crop Mapping using Machine Learning and Sentinel-2 Satellite Imagery Zhao, Kui Wu, Siyang Liu, Chang Wu, Yue Efremova, Natalia Computer Vision and Pattern Recognition Image and Video Processing Food security has grown in significance due to the changing climate and its warming effects. To support the rising demand for agricultural products and to minimize the negative impact of climate change and mass cultivation, precision agriculture has become increasingly important for crop cultivation. This study employs deep learning and pixel-based machine learning methods to accurately segment lavender fields for precision agriculture, utilizing various spectral band combinations extracted from Sentinel-2 satellite imagery. Our fine-tuned final model, a U-Net architecture, can achieve a Dice coefficient of 0.8324. Additionally, our investigation highlights the unexpected efficacy of the pixel-based method and the RGB spectral band combination in this task. |
| title | Precision Agriculture: Crop Mapping using Machine Learning and Sentinel-2 Satellite Imagery |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2403.09651 |