## 5 Conclusions  
The modeling of predictive processes based on machine learning or deep learning requires a section for properly selecting features. These methodologies can be categorized into three distinct selection types, namely _filter_, _wrapper_, and _embedded_ techniques. In this study, our focus converges on a wrapper approach, based on results retrieved from a recent publication [17]. On several occasions, the application of exhaustive search mechanisms can provide an immense number of feature combinations that are equally efficient at predicting a particular phenomenon, such as the phenological state in this case. The main goal of this investigation has been to analyze two distinct methods to extract useful information from the particular case presented in [17], gaining practical knowledge in a format of a reduced and representative group of characteristics. One of the methods consists of using metrics derived from market basket analysis, which is popularly used to explore the associations between purchased objects, or the properties of groupings in a more generalized case. In the present study, it has been observed that the utilization of this approach, in conjunction with another more basic technique, has facilitated the extraction of a central characteristic set. The value of limiting the original data set to groups containing this central collection lies in the improved efficiency of the resulting group. Furthermore, rather than combining the implementation of both methods, the independent application terminates in two distinct sets of specific  
Figure 6: Accuracy comparison for distinct feature selection methodologies. features. Both feature combinations represent high accuracy levels, thus validating the strategy for selecting a representative and efficient subset of features. In conclusion, the proposed methodologies are useful for selecting a group of robust characteristics for the phenological prediction of olive trees. In order to extend this strategy to other predictions, it is important to remember that this wrapper-kind feature selection process is intrinsically linked to a specific prediction model. This implies that the transference of this tactic to other modeling procedures requires a preliminary assessment to determine a prediction model appropriate to the data and the context. Nonetheless, it is significant that efficiency of the combination of features obtained in this procedure is similar to that offered by standard processing techniques. A noteworthy limitation of this evidence is that the running time of the whole process (creating all possible combinations and then analyzing them using MBA techniques) may be unnecessarily costly. Therefore, its implementation would be recommended for non-urgent contexts, in situations that value robustness and stability of the results apart from the accuracy. Conceptualization and methodology, software and formal analysis, I.A. ; validation and investigation, I.A. ; writing--original-draft preparation, I.A. ; review and editing, I.G.O. and M.Q. ; supervision, I.G.O. and M.Q. ; project administration and funding acquisition, I.G.O. and M.Q. All authors have read and agreed to the published version of the manuscript. This material is based upon work funded by the H2020 DEMETER project, grant agreement ID 857202, funded under H2020-EU.2.1.1. Not applicable. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
GDD  
Growing degree days. MBA  
Market Basket Analysis. BBCH  
Biologische Bundesanstalt, Bundessortenamt, und CHemische Industrie. DOY  
Day Of Year. EVI  
Enhanced vegetation index. NDVI  
Normalized difference vegetation index. GE  
Google Earth Engine. RMSE  
Root-mean-square deviation. M1  
Metric 1, 1-RMSE weight proportional metric. M2  
Metric 2, MBA-derived metric  
G1  
Group 1, feature grouping derived from M1. G2  
Group 2, feature grouping derived from M2. G3  
Group 3, feature grouping derived from Pearson correlation coefficient.