## 4 Conclusions  
This study aimed to evaluate the combinations of seven vegetation indices from Sentinel-2 images and seven phenology metrics for the yield potential prediction of maize and soybean, narrowing the research gap regarding the lack of knowledge on the effectiveness of phenological modeling in crop yield potential prediction using machine learning. The main conclusions, based on the results of comprehensive machine learning predictions of maize and soybean yield potential during four consecutive years (2019-2022) are as follows:  
* RF consistently outperformed the SVM, MARS, and BRNNs in both the cross-validation and final model fit accuracy assessment metrics, indicating its superior ability to capture nonlinear relationships in derived phenological indicators for yield prediction. * While RF's cross-validation R\({}^{2}\) was moderately high (up to 0.409 in 2019), its final fit R\({}^{2}\) of 0.898 suggests that the full training set provided a substantial benefit to model learning but this observation leaves an ambiguity in the discrepancy between cross-validation and final model fit metrics, with no definite knowledge on expected prediction accuracy when using new, unseen datasets. * The phenological metric related to the vegetation index values at POS was the most important for the prediction of yield potential in both maize and soybean, closely followed by maturity and senescence. However, temporal components of phenological modeling, quantified as DOYs of occurrence of evaluated phenological metrics, produced the most influential covariates, with DOY (EOS) and DOY (SOS) being crucial for the yield potential prediction in particular years for maize and soybean, respectively. * NDVI was consistently the most important predictor for the maize yield potential prediction across all years, while NDVI3 RE, which is characterized by an increased resistance to the saturation effect due to the increased leaf area index, was the dominant vegetation index for the soybean yield potential prediction. While phenological modeling combined with Sentinel-2-derived vegetation indices enabled the yield potential prediction of maize and soybean within the study area and period, the overall effectiveness of this approach should be further validated using independent, sensor-based yield measurements. Conceptualization, D.R. ; methodology, D.R. ; software, D.R. ; validation, I.P. and M.J.; formal analysis, D.R. ; investigation, D.R. ; resources, D.R. ; data curation, D.R. ; writing--original draft preparation, D.R. ; writing--review and editing, D.R., I.P. and M.J.; visualization, D.R. ; supervision, I.P. and M.J.; project administration, D.R. ; funding acquisition, D.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available upon request from the corresponding author. This research was supported by the project \"Soybean cropland suitability prediction based on machine learning regression\" from the research team \"Technical and technological systems in agriculture, GIT, precision agriculture, and environment protection\" of the Faculty of Agrobiotechnical Sciences Osijek. The authors declare no conflicts of interest. * [PERSON] (2024) [PERSON]; [PERSON] AI for Data-Driven Decision-Making in Smart Agriculture: From Field to Farm Management. In _Artificial Intelligence Techniques in Smart Agriculture_; [PERSON], [PERSON], [PERSON], [PERSON]; Springer Nature: Singapore, 2024; pp. 173-193, ISBN 978-981-97-5878-4. * [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Artificial Intelligence Tools for the Agriculture Value Chain: Status and Prospects. _Electronics_**2024**, _13_, 4362. [CrossRef]
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