## 6 Conclusions  
The presented paper completes the research in the field of intelligent agro-meteorological decisions to address the challenges of ensuring the sustainable provision of food crops in the context of climate change. Although there are multiple studies in the field, a new, unique approach that addresses missing data is needed.  
The primary objective of our research was to improve the efficiency of sunflower cultivation by developing a yield prediction model that relies on the selection of an optimal planting day, guided by a region-specific weather forecast. In order to construct a regression model capable of accurately predicting crop yield, we aggregated several datasets encompassing information pertaining to crops, planting dates, crop varieties, geographical locations, and historical yield records. Additionally, meteorological parameters such as precipitation and temperature were incorporated into the model as predictors.  
Moreover, in our experiments, we performed a comparative study of nine imputation methods, which were evaluated on two regression algorithms. We obtained an increase in the model performance for both regression algorithms. The best results were obtained using the prediction method for imputation, especially the RF. The second set of experiments involved unsupervised outlier removal algorithms, in which four methods were compared with various contamination rates. Overall, results were improved in most cases with statistical significance. The best \(R^{2}=0.938\) and \(MAE=77.689\) were obtained using the Oc-SVM outlier removal and RF imputation algorithms, for the model built with HGBR. An important finding is that a very significant improvement was mainly achieved through preprocessing methods, compared to the initial results without imputation or outlier removal (\(R^{2}=0.723\)).  
We have been able to achieve our research objective and build an accurate model based on limited data, which could support agricultural decisions to mitigate the effects of climate change and maximise production. Future research directions involve incorporating more variables as predictors, such as the use of fertilisers or pest control. There is a substantial need to collect crop datasets specific to this purpose from reliable sources to help train models for specific geographical locations and crops. As a further exploration of the impact of the planting day and weather conditions on the crop yield, we aim to also consider employing forecasting methodologies and assessing the reliability of using the same or similar preprocessing methods to improve the model performance.  
**Author Contributions:** All authors have contributed equally to this research. Conceptualization, A.D.C., A.M.C. and H.B.M.; Data curation, A.D.C.; Methodology, A.D.C., A.M.C. and H.B.M.; Software, A.D.C.; Validation, A.D.C., A.M.C. and H.B.M.; Writing--original draft, A.D.C.; Writing--review & editing, A.M.C. and H.B.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The publication of this article was supported by the 2022 Development Fund of the UBB.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analysed in this study. This data can be found here: USDA for crop data [[https://quickstats.nass.usda.gov/](https://quickstats.nass.usda.gov/)]([https://quickstats.nass.usda.gov/](https://quickstats.nass.usda.gov/)) (accessed on 10 October 2022) [27], and DayMet for meteo data [[https://daymet.ornl.gov/](https://daymet.ornl.gov/)]([https://daymet.ornl.gov/](https://daymet.ornl.gov/)) (accessed on 16 January 2023) [28].  
**Conflicts of Interest:** The authors declare no conflict of interest.