It provides farmers with accurate information, helps them anticipate problems, maximize yields, and develop more efficient and sustainable solutions. In short, a DT is the key to driving efficiency and productivity in Agriculture 5.0. In [PERSON] et al. [9], the authors highlight the significance of DTs in agriculture as a virtual representation of farms with the potential to enhance productivity, efficiency, and energy conservation. They offer an overview of the current state of the art in DT concepts and technologies within agricultural contexts. The review outlines a comprehensive framework for DTs encompassing soil management, irrigation, robotics, farm machinery, and post-harvest food processing. It delves into various aspects such as data recording (whose analysis allows the prediction of crop performance [10]), modeling (including AIand big data), simulation, analysis, prediction, and communication (e.g., IoT and wireless technologies) within the agricultural DT framework. The paper concludes by emphasizing the role of DT systems in supporting farmers through continuous real-time monitoring of the physical and virtual farm environments, ushering in the next generation of digitalization in agriculture. The paper by [PERSON] et al. [11] establishes a threefold agenda, proposing a farm-tailored framework for smart agriculture and DTs, exemplifying its implementation through a nitrogen fertilization case study, and outlining challenges and future potentials. The framework, structured around data collection, processing, analysis, and application, employs an infological approach. The case study demonstrates the framework's utility in optimizing nitrogen fertilization by addressing spatial and temporal variations in land, soil, and crop factors. The study emphasizes the role of DTs in predictive analyses, ultimately benefiting agricultural sustainability and underscoring their potential for small-farm regions. In refs. [8; 12], the authors offer a literature search, in which the first appearance of the application of the concept of DT to the field of agriculture was in 2017. In these papers, they show several examples, such as a prototype of a DT of a greenhouse in which they use information collected by a robot, for example on soil nutrients and humidity. Another example of a DT is on simulations of a garden to know what the robot should do to ensure that the crop is in the ideal conditions to grow [13]. Also, a DT of a tomato crop is also presented in [14], whose developer team set up a 3D model of the crop in order to add real-time information from sensors, also with the aim of making the right decisions to ensure that the real crop grows in the best conditions. In ref. [15], it is shown a prototype of a DT on a smart farm, where the authors perform tests of their system with an example plant. Also, in future work, the authors indicate that such a system will be applied to larger projects, using AI (artificial intelligence), to enable sustainable development and improve food security (and traceability). Also, in [16], the application of AI to point clouds in agriculture helps to control cropping in vineyards. The paper [17] introduces a novel decision support system tailored to urban farming production, emphasizing distinctions from traditional agribusiness requirements. The focus is on urban agriculture, particularly aquaonics, a method integrating plant and fish cultivation in a water-efficient cycle. The study employs a cyber-physical implementation of aquaonics, enhanced by a digital twin system and machine learning for adaptive capabilities. Empirical results from a three-month trial showcase the effectiveness of data-driven decision analytics and a digital twin model in planning aquaponic system production. Additionally, the article proposes a modeling framework for large-scale urban agriculture ecosystems, forming the foundation for a decision support system that coordinates activities among independent producers to achieve collective goals.