A Fuzzy Based Predictive Approach for Soil Classification of Agricultural Land for the Efficient Cultivation and Harvesting
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-36118-0_64
Reference19 articles.
1. van Klompenburg, T., Kassahun, A., Catal, C.: Crop yield prediction using machine learning: a systematic literature review. Comput. Electron. Agric. 177, 105709 (2020)
2. Rahman, S.K., Zaminur, A., Chandra Mitra, K., Mohidul Islam, S.M: Soil classification using machine learning methods and crop suggestion based on soil series. In: 2018 21st International Conference of Computer and Information Technology (ICCIT), IEEE (2018)
3. Meier, M., et al.:Digital soil mapping using machine learning algorithms in a tropical mountainous area. Revista Brasileira de Ciência do Solo 42 (2018)
4. Wadoux, A.M.J.-C., Minasny, B., McBratney, A.B.: Machine learning for digital soil mapping: applications, challenges and suggested solutions. Earth-Sci. Rev. 210, 103359 (2020)
5. Kingsley, J., et al.: Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil. Land 9(12), 487 (2020)
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