## 4 Conclusions  
In this paper, we explored the advantages of integrating digital agricultural models, particularly through photogrammetry techniques. We conducted an extensive study to assess the benefits of utilizing photogrammetry for the creation of digital twins (DTs) in the agricultural domain. Subsequently, we performed both qualitative and quantitative analyses of the four most commonly used tools for implementing these photogrammetric techniques and generating 3D models. The quantitative assessment was conducted in terms of the evaluation of the different quality metrics, such as curvature, anisotropy, linearity, flatness, and sphericity. The \begin{table}
\begin{tabular}{l c c c c} \hline  & **Estimate** & **Std. Error** & **t Value** & **Pr (\(>|t|\))** \\ \hline (Intercept) & 1.2434 & 1.8106 & 0.687 & 0.530 \\ Sph & 1.4504 & 0.7347 & 1.974 & 0.120 \\ Pla & \(-\)0.2409 & 2.2934 & \(-\)0.105 & 0.921 \\ Cur & \(-\)0.1439 & 1.2560 & \(-\)0.115 & 0.914 \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of the multilinear analysis with all the CVs used to model the synthetic quality metric with 3 parameters. \begin{table}
\begin{tabular}{l c} \hline
**Software** & **Multi-Linear Model** \\ \hline Agisoft & 1.6509 \\ Pix4D Mapper & 2.4437 \\ ODM & 2.6596 \\ DJI Terra & 1.2895 \\ Meshroom & 2.1059 \\ Regard3D & 2.2601 \\ RealityCapture (EDU) & 2.1510 \\
3 DF Zephyr & 2.3641 \\ \hline \end{tabular}
\end{table} 
Table 5: Synthetic quality metric as a result of the application of the multilinear model to the CVs (with 3 parameters). correlation of the coefficients of variation for every piece of software within each quality metric allowed us to determine that a model with three of these metrics (i.e., sphericity, planarity/flatness and curvature) was optimum for the generation of a new synthetic metric. Also, the qualitative assessment of the different tools enabled the evaluation of the main characteristics that were of interest to us (i.e., free software, availability of API, georeferencing). These quantitative and qualitative metrics were used to model a synthetic quality metric which involved all the aforementioned aspects. The evaluation of the synthetic metric allowed us to categorize the different software tools with the inclusion of the three qualitative aspects, which modulated the synthetic quality model, weighting these aspects up to a certain level (here 85%) if they were not available in the software. The evaluation ranking identified OpenDroneMap as the best option considering that it is open-source, georeferenced, and has an API to process the point clouds. Our comprehensive evaluation of the study leads us to conclude that the OpenDroneMap (ODM) tool presents the best quality-to-features ratio, and it is the software that shows the best cost-benefit relationship. Notably, it offers high-quality results, is an open-source tool with continuous improvements, and boasts faster computation times. Furthermore, the tool's versatile features make it adaptable for a wide range of projects in the agricultural sector. Agriculture has always been an essential part of mankind, providing food, livelihoods, and contributing to socio-economic development [33]. The relationship between Agriculture 5.0 and society is multifaceted and encompasses various aspects. Beyond Agriculture 5.0, there is a strong connection between agriculture and society, this union between society and the new era of digital agriculture could be called Agriculture 6.0. In Agriculture 6.0, the integration of DTs will improve education and knowledge exchange. DTs serve as virtual representations of agricultural systems, enabling real-time data exchange and decision-making. Using machine learning and sensor data, DTs can gather information about physical models and create accurate virtual representations. This facilitates knowledge transfer among farmers, researchers, and stakeholders, allowing them to better understand complex agricultural processes.