Another work related to aquaponics is [18], where the authors propose and deploy a whole efficient system for the feeding cycle in fish farms and the generation of plant nutrients. This system has proven to provide good aquaponics life-cycle monitoring metrics with different communication technologies and has also allowed a reduction in energy consumption to make the system more sustainable. Another work based on smart farming [19] is about the design and implementation of a farmer's digital twin, in which the authors have wearable sensors that \"are used to acquire real-time data on bone rotation from a farmer's body\", to control the farmer's activity. Agrilora [20] is a project of a digital twin for smart agriculture, with the objective of increasing crop yields by addressing the problem of high maintenance and hardware costs, deploying wireless sensors in the field to detect plant diseases. Many of the works dealing with this DT idea are conceptual, such as the one developed in [21], where the idea of a service/product for agriculture is shown, where artificial intelligence and a DT are used to make decisions for the correct management of a greenhouse. The DT has the function of controlling the environmental conditions of the greenhouse, such as humidity and \(CO_{2}\) level, as well as the possibility to control the windows and fans of the greenhouse itself. Nowadays, drones have become an invaluable source of data for activities such as inspection, surveillance, mapping, and 3D modeling [22]. Through a conventional photogrammetric process, it is possible to obtain three-dimensional results, such as digital surface models or digital terrain models (DSM/DTM), elevation contours, 3D textured models, and vector data, among others, in a highly automated manner. These data can be used in a wide variety of situations and scenarios. The use of photogrammetry allows the development of 3D point cloud maps. Through this technique, in [23], an unsupervised method of vineyard detection and feature extraction from 3D point cloud maps is presented. The proposed method allows the automatic generation of maps of the regions of land covered by vineyards and, in addition, provides information on the local orientation of the vine rows and the spacing between rows, spatially organized in maps. Another example is [24], where an alternative approach is presented to estimate the correct time of corn harvest; the proposed method focuses on the relationship between the maturity data obtained by photogrammetry and the parameters produced by the chemical analysis of maize. Another work where a photogrammetric approach is developed is [25]. In that paper, the authors develop an automatic system to measure the roughness of the ground surface from images taken on the ground with a simple digital camera, without geometric restrictions. The accuracy of the system was determined on artificial models built with polystyrene, whose position and elevation accuracies were approximately 1.5 mm, while the error in surface estimation was less than 0.76% of the site surface. These results show that two roughness indices, the surface tortuosity index and the mean value of the height, are the most effective for discriminating the levels of tillage of the agricultural soil. In [26], the authors also use photogrammetry but for monitoring fruits at different growth stages. In this case, they estimate automatically the size of an \"in-field\" apple, generating a point cloud \"using structure-from-motion (SfM) and multi-view stereo (MVS)\" and estimating the size of the fruit. The downside of the job is the long processing time. Most of the work found is conceptual, although some prototypes have been tested. Throughout the search, it was observed that none of the articles talks in detail about the tools/technologies they intend to use when developing the DT from photogrammetric techniques.