Intelligent organic matter prediction of agriculture soil using satellite images
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-18955-w.pdf
Reference31 articles.
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2. Gulhane VA, Rode SV, Pande CB (2023) Correlation analysis of soil nutrients and prediction model through ISO cluster unsupervised classification with multispectral data. Multimed Tools Appl 82:2165–2184. https://doi.org/10.1007/s11042-022-13276-2
3. Yadavendra CS (2022) Semantic segmentation and detection of satellite objects using U-Net model of deep learning. Multimed Tools Appl 81:44291–44310. https://doi.org/10.1007/s11042-022-12892-2
4. Bakhshandeh E, Hossieni M, Zeraatpisheh M, Francaviglia R (2019) Land use change effects on soil quality and biological fertility: a case study in northern Iran. Eur J Soil Biol 95:103119. https://doi.org/10.1016/j.ejsobi.2019.103119
5. Xu X, Du C, Ma F, Qiu Z, Zhou J (2023) A Framework for high-resolution mapping of Soil Organic Matter (SOM) by the integration of Fourier Mid-Infrared Attenuation Total Reflectance Spectroscopy (FTIR-ATR), sentinel-2 images, and DEM derivatives. Remote Sens 15(4):1072. https://doi.org/10.3390/rs15041072
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