# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 5 Conclusions  
In this study, the monitoring of wheat crops, including both durum and soft wheat, was conducted throughout the growing season to investigate the relationship between yield and the independent variable \"Area Under the Curve\" (AUC) of vegetation indices. This research demonstrates the effectiveness of specific vegetation indices (VIs) in estimating wheat yield, with GNDVI emerging as the most reliable predictor for durum wheat and LAI for soft wheat. Phenological stages are widely recognized for their pivotal role in determining crop productivity, and the integration of AUC offers a robust method for correlating yield with dynamic changes observed across these stages. The findings of this study highlight the significance of specific phenological stages for yield prediction. The results indicate that the time period spanning from Tillering to Stem Elongation and extending through Grain Filling plays a pivotal role in wheat crop yield monitoring. This observation aligns with the existing literature reinforcing the critical importance of these growth stages in determining crop productivity. The results of this investigation emphasize the resilience of the suggested methodology; however, additional inquiry is essential to enhance its relevance. The expansion of the dataset to incorporate a larger array of wheat cultivation areas, additional growth periods, and diverse geographic regions will substantially enhance the external validity and rigor of the results. Such efforts will provide deeper insights into the spatial and temporal dynamics of wheat crop yield prediction, paving the way for more comprehensive monitoring and management strategies in varied agricultural contexts. While the proposed framework shows strong predictive capabilities, certain limitations exist, including the impact of adverse weather conditions on data availability, spatial resolution constraints, and potential interference from weeds. Additionally, the web application exemplifies a robust and user-centered methodology for agricultural monitoring, providing real-time actionable intelligence while maintaining cost-efficiency and accessibility. Its capacity to facilitate data-informed decision making accentuates its significance in endorsing sustainable practices and enhancing agricultural productivity. The integration of artificial intelligence for geospatial data analysis signifies a promising pathway for augmenting the application's functionalities, thereby laying the groundwork for more comprehensive and intelligent solutions within the realm of precision agriculture.  
Conceptualization, K.P. and K.N.; methodology, K.N.; software, G.G.; validation, K.P. and K.N.; formal analysis, K.N.; investigation, V.D.-P.; resources, V.D.-P.; data curation, V.D.-P. and G.G.; writing--original draft preparation, K.P.; writing--review and editing, K.P. and K.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are not available for publication as they remain the private property of the farmer.  
The authors express their gratitude to [PERSON], a dedicated farmer, for generously providing the invaluable data that made this study possible.  
Author [PERSON] was employed by NubiGroup Geoservices & Research Private Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.