## 5 Conclusions
Xgboost: Extreme Gradient Boosting. Available online: [[https://cran.r-project.org/web/packages/xgboost/index.html](https://cran.r-project.org/web/packages/xgboost/index.html)]([https://cran.r-project.org/web/packages/xgboost/index.html](https://cran.r-project.org/web/packages/xgboost/index.html)) (accessed on 14 October 2024). * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Determining Leaf Nutrient Concentrations in Citrus Trees Using UAV Imagery and Machine Learning. _Precis. Agric._**2022**, _23_, 854-875. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] A Machine Learning Framework for Mapping Soil Nutrients with Multi-Source Data Fusion. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11-16 July 2021; pp. 3705-3708. * _[PERSON] et al. (2024)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Geospatial Prediction of Total Soil Carbon in European Agricultural Land Based on Deep Learning. _Sci. Total Environ._**2024**, _912_, 169647. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Ensemble Deep Learning: A Review. _Eng. Appl. Artif. Intell._**2022**, _115_, 105151. [CrossRef]
* _[PERSON] et al. (2024)_ [PERSON]; [PERSON] [PERSON] Ensemble Machine Learning Prediction of Anaerobic Co-Digestion of Manure and Thermally Pretreated Harvest Residues. _Bioresour. Technol._**2024**, _402_, 130793. [CrossRef]
* _[PERSON] et al. (2024)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; et al. Caret: Classification and Regression Training. Available online: [[https://CRAN.R-project.org/package=caret](https://CRAN.R-project.org/package=caret)]([https://CRAN.R-project.org/package=caret](https://CRAN.R-project.org/package=caret)) (accessed on 30 November 2024). * _[PERSON] et al. (2023)_ [PERSON]; [PERSON]; [PERSON] Simultaneous Multi-Crop Land Suitability Prediction from Remote Sensing Data Using Semi-Supervised Learning. _Sci. Rep._**2023**, _13_, 6823. [CrossRef]
* _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON] Understanding the Rapid Summer Warming and Changes in Temperature Extremes since the Mid-1990s over Western Europe. _Clim. Dyn._**2017**, _48_, 1537-1554. [CrossRef]
* [PERSON] (2012) [PERSON] Ensemble Learning. In _Ensemble Machine Learning: Methods and Applications_; [PERSON], [PERSON], Eds. ; Springer: New York, NY, USA, 2012; pp. 1-34, ISBN 978-1-4419-9326-7. * _[PERSON] et al. (2021)_ [PERSON]; [PERSON] Approximating XGBoost with an Interpretable Decision Tree. _Inf. Sci._**2021**, _572_, 522-542. [CrossRef]
* _[PERSON] et al. (2025)_ [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON] Iml: Interpretable Machine Learning. Available online: [[https://cran.r-project.org/web/packages/iml/vignettes/intro.html](https://cran.r-project.org/web/packages/iml/vignettes/intro.html)]([https://cran.r-project.org/web/packages/iml/vignettes/intro.html](https://cran.r-project.org/web/packages/iml/vignettes/intro.html)) (accessed on 1 January 2025). * _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Can We Use Machine Learning for Agricultural Land: Suitability Assessment? _Agronomy_**2021**, _11_, 703. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Cropland Suitability Assessment Using Satellite-Based Biophysical Vegetation Properties and Machine Learning. _Agronomy_**2021**, _11_, 1620. [CrossRef]  
**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.