## 4 Conclusions  
The integration of RS, WSN, and DL within the framework of this study successfully allowed evaluating to what extent it was possible to diagnose the CLR development stage in the Colombian Caturra variety. To this end, the most relevant information obtained was consolidated, the knowledge about the study context and CLR was detailed, and the repercussions of the disease in the Colombian coffee growing industry were identified. Furthermore, the state-of-the-art methods were reviewed and used for the current research. Creative design sessions were carried out to define the most useful technological integration of RS and WSN. Afterward, a functional prototype that automatically collected data in the field and transferred them to a remote server over the Internet was built. Besides, a diagnostic model using DL was implemented based on the stored data, and it successfully allowed evaluating the CLR development stage with unknown field data. The motivation of this research project was to contribute to rural development through technological innovation to strengthen the profitability of Colombian coffee growers. Considering that the country has the potential, in terms of environmental conditions and diverse ecosystems, to generate a giant portfolio of exotic products that would be better valued in the specialty coffee market, this research evaluated, with empirical evidence, a technological approach that attempted to facilitate the diagnosis and mitigate the risks of one of the most economically limiting diseases for coffee production. In that sense, the proposed technological integration could positively impact the rural sector since those innovations promote investments in infrastructure, which are crucial to empower the rural community and improve the living standards and activities concerning progress, productivity, and income generation. The obtained \(p\)-value in the analysis of the results was 0.231, which helped to determine, with 90% confidence, that the visual inspection and the technological integration did not present a statistically significant difference regarding the diagnosis of the CLR development stage. Thus, it could be said that the assessment of the disease led to a similar outcome using either method, which suggested that the obtained results supported the research hypothesis. Finally, it could be asserted that through the integration of RS, WSN, and DL, it was possible to diagnose the CLR development stage in the Colombian Caturra variety with a \(F_{1}\)-score of 0.775. This value indicated that, on average, the diagnostic model was excellent in terms of the certainty and usefulness of its diagnosis. Regarding the data processing phase, a further extension of this research could include the implementation of a simple user interface for visualizing the diagnosis of the CLR development stage through the generated DL model to better illustrate the results to a coffee grower. Additionally, the proposed technological integration could be scaled to a real context by using drones with one or both of the two multispectral cameras used in the experiment presented by this work (depending on the project budget) as a possible approach, knowing that the identification of the CLR could be done with just one camera, e.g., RGN (\(F_{1}\)-score of 0.946), due to its high score. Another real context approach could be further explored using a mobile autonomous robot with a single RGB camera. Finally, the \(F_{1}\)-score values achieved on the test set, which showed that the submodels based on images presented a higher performance than the JSON submodel (sensor data model), suggested reconsidering the composite model for future work and focusing all efforts on improving the collection and processing of just RGB and multispectral data or using more robust sensors when the technology