# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 1 Introduction
### Related Work in Other Countries/Latitudes  
[PERSON] et al. [18] present an overview of how wireless technologies are transforming agriculture, especially with the advent of advanced networks such as 6G. The authors present major advances in both hardware--such as antennas and high-capacity systems such as massive MIMO--and software, where AI is being used to optimize and improve the accuracy of agricultural monitoring. The authors argue that China is betting on the development of 6G to take agriculture to a new level of production. China's efforts go beyond boosting connectivity to achieve its goal of merging the physical, biological, and digital to build smarter, more automated agricultural systems. With 6G, farmers could monitor crops in real time, using advanced sensors, deploying drones to spot pests or plant issues, and managing resources like water and fertilizer far more efficiently. [PERSON] et al. [1] present the evolution towards Agriculture 5.0, where technologies like AI, the IoT, robotics, AR, blockchain, and 6G are reshaping farming into a more precise and sustainable activity. Their study also emphasizes the role of techniques such as transfer learning to adapt AI to limited-resource environments and stresses the need for ethical and regulatory frameworks to address data privacy and fairness. The paper identifies challenges to the adoption of these technologies, including cost, farmer behavior, and data ownership. Finally, it identifies future directions such as seamless integration of technologies, autonomous robotic systems, and personalized decision support to optimize farm management and supply chains. [PERSON] et al. [3] explore recent technological advances in precision agriculture and automation, with an emphasis on creating sustainable and efficient responses to current challenges in the farming sector. They cover a range of developments, including the use of robotics for harvesting fruits like strawberries, tomatoes, and apples; the application of machine learning (ML) and computer vision for detecting pests and diseases; mechanical and sensor-based methods for weed control; and the deployment of coordinated fleets or swarms of agricultural robots. Their study also addresses the evolution towards Agriculture 5.0 (Ag5.0), which integrates AI, ML, advanced robotics, the IoT, blockchain and next-generation communication technologies (such as 6G). These technologies enable highlydigitized, sustainable, and human-centered farm management, with applications such as autonomous robots, drones, digital twins, and human-robot collaborative systems. The authors highlight the use of deep learning models for tasks such as disease detection, weed monitoring, nutrient stress assessment, and crop yield prediction, with accuracies in excess of 90% in some cases. Current challenges are discussed, such as data standardization, integration of different crop types, and the need for adequate infrastructure for mass adoption of these technologies. On the other hand, [PERSON] et al. (2018) present Agriculture 5.0 as an evolution of the agricultural sector that integrates advanced technologies such as AI, the IoT, robotics, blockchain, and cloud computing to improve productivity, sustainability, and resilience in the face of challenges such as climate change, food insecurity, and labor shortages. Unlike Agriculture 4.0, this new paradigm focuses on human-machine collaboration and a human-centric approach that promotes mass customization, skilled job creation, and sustainable agricultural practices within the broader framework of Society 5.0, a super-smart and inclusive society. In this context, the above background proves that Agriculture 5.0, driven by advanced technologies such as AI, the IoT, robotics and next-generation networks such as 6G, is driving a profound transformation of the agricultural sector, orienting it towards smarter, automated, and sustainable systems, thus consolidating agriculture as a sector in constant evolution toward a highly digitalized and efficient model.