## 5 Conclusions  
Our research in precision agriculture utilizing UAVs with multispectral and thermal imaging during two flights (carried out in June and September, respectively) provided valuable insights into the relationship between various vegetation indices (NDVI, GNDVI, NDE, and CWSI) and pistachio yield and quality. Notwithstanding NDVI's saturation issues, all VIs demonstrated significant correlations with yield and quality parameters, particularly during the nut development stages (F1-F2). In June, the highest correlations included NDRE and carbohydrates (r = 0.78) and CWSI and oleic acid (r = 0.77), and non-thermal VIs, especially NDVI and GNDVI, correlated well with yield but less well with nutritional aspects. In this sense, NDRE was more effective. In contrast, September's top correlations were CWSI and carbohydrates (r = 0.62) and NDVI and iron (r = 0.54), and flight imagery revealed that only GNDVI displayed a strong correlation with both agronomic and quality parameters. Notably, CWSI showed considerable correlations in both flights with quality parameters like carbohydrate percentage, magnesium, iron, and several fatty acids (palmitoyl, stearic, oleic, and linoleic), surpassing non-thermal-based vegetation indices. The different results observed in the CWSI compared to other vegetation indices highlight the usefulness of all bands of the electromagnetic spectrum caused by the divergent behaviors between temperature-based and non-temperature-based indices. Our research highlights the potential for the CWSI to be a key tool for assessing pistachio quality. The impact of environmental factors, like the sun's location, is also significantly emphasized in the study, illustrating the importance of taking these elements into account when interpreting the CWSI. Remote sensing techniques not only revealed a significant correlation with yield parameters but also with nutritional values, specifically fatty acids. This crucial information could potentially fine-tune the timing of harvest, a critical determinant of pistachio quality. These correlations changed depending on the time of year at which the drone flight was conducted (which is linked to the phenological state of the pistachio tree) and on the vegetation index used, thus underlining the importance of devising a well-planned strategy that incorporates several aerial surveys throughout the year and investigates multiple indices for effective pistachio crop management. The complexity of these relationships requires further research, particularly integrating multiple indices in a time-series analysis to gain a more extensive understanding of yield and stress evolution in pistachio trees. Conceptualization, R.M.-P. and S.A.; methodology, R.M.-P., S.V., H.M. and S.A.; software, R.M.-P., S.V. and R.V. ; validation, R.M.-P.; formal analysis, R.M.-P. and S.V. ; investigation, R.M.-P.; resources, R.M.-P., R.V. and H.M.; data curation, R.M.-P., S.V., R.V. and H.M.; writing--original draft preparation, R.M.-P.; writing--review and editing, R.M.-P., S.V., R.V. and S.A.; visualization, R.M.-P.; supervision, S.A.; project administration, S.A.; funding acquisition, S.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the project CDTI (IDI-20200822) and by MCIN/ AEI/10.13039/ 501100011033 and European Union <<NextGenerationEU>> /PPRTR, grant number RYC2021-033890. Co-financed by FEADER funds and Junta de Castilla y Leon (Spain). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request.