# Comparative Evaluation of Remote Sensing Platforms for Almond Yield Prediction
## 5 Conclusions  
This study investigates the potential of RS data and ML models for predicting almond yield. Various RS platforms were evaluated, including both freely available platforms, including MODIS Terra LST, GSMaP, Landsat 8, and Sentinel-2, as well as a paid platform, PlanetScope. In addition, the performance of several ML regressors, including RFR, XGBR, GBR, BR, and ABR, were evaluated. The inclusion of high-resolution VIs from the PlanetScope platform significantly increased the accuracy of almond yield prediction. The XGBR model trained with a feature set comprising irrigation data, the daily temperature in March, the NDVI in January, and the SAVI in May from the PlanetScope platform showed the highest predictive performance, achieving an \(R^{2}\) value of 0.80. This indicates that the model could effectively explain 80% of the variation in the almond yield. However, freely available RS platforms, such as MODIS Terra LST and GSMaP, can also serve as viable alternatives to PlanetScope data. Despite the lower spatial resolution, the data from these platforms demonstrated that it still provides valuable insights for predicting almond yield. It is worth noting, however, that the choice of ML model was found to be a critical factor in the prediction accuracy. While the XGBR model consistently outperformed the other models, it proved more prone to noise and outliers when only one or two types of features were used. Therefore, the selection of the most suitable ML algorithm should be based on the dataset and features to be considered. Irrigation and the daytime temperature in March were among the most important features for predicting almond yield, highlighting the pivotal role of water and temperature in crop growth and development. Future research may be aimed at the continuous improvement of the dataset implemented in this study, by increasing the number of almond orchards by considering broader geographical areas and including established climatic and temporal relationships with the yield in the evaluated orchards. This will improve the generalization ability of the models. It would also be useful to consider very-high resolution UAV multispectral data to provide tree-level almond yield. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/agriengineering6010015/sl](https://www.mdpi.com/article/10.3390/agriengineering6010015/sl)]([https://www.mdpi.com/article/10.3390/agriengineering6010015/sl](https://www.mdpi.com/article/10.3390/agriengineering6010015/sl)); Figure S1. Monthly average land surface temperature (left panels) and monthly precipitation sum, for 2017 to 2021, in the studied almond orchards: (a) almond grower 1, (b) almond grower 2, (c) almond grower 3, (d) almond grower 4. **Author Contributions:** Conceptualization, N.G. and J.J.S. ; Methodology, N.G. and A.B. ; Software, N.G. and H.F.; Validation, N.G. and J.J.S. ; Formal analysis, N.G., H.F. and P.C. ; Investigation, N.G., H.F. and A.B. ; Resources, N.G., H.F. and L.P.; Data curation, N.G., H.F. and L.P.; Writing--original draft, N.G. ; Writing--review and editing, N.G., H.F., J.J.S., L.P., A.B. and P.C. ; Visualization, N.G., H.F. and L.P.; Supervision, J.J.S., A.B. and P.C. ; Project administration, J.J.S. ; Funding acquisition, J.J.S. and P.C. All authors have read and agreed to the published version of the manuscript. **Funding:** Financial support was provided by national funds through FCT--Portuguese Foundation for Science and Technology (UI/BD/150727/2020), under the Doctoral Programme \"Agricultural Production Chains--from fork to farm\" (PD/00122/2012), and from European Social Funds and the Regional Operational Programme Norte 2020.