# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 4 The Future of Farming: 6G-IoT Applications in Agriculture 5.0
### More Accurate and Reliable AI-Based Applications  
The use of AI in Agriculture 5.0 can lead to innovative applications such as autonomous farm robots and farming systems. Furthermore, this rather new field of research offers new possibilities for creating more robust smart agricultural applications, ranging from weather or crop yield prediction and forecasting [117; 118] to video monitoring and remote crop diagnostics [112], thus fulfilling the basic requirements of Agriculture 5.0.  
However, as stated in the preceding subsections, the introduction of 6G-IoT can significantly improve the measurement process in terms of the quantity and quality of measured data, as well as by providing greater coverage even in geographically remote areas. Gathering the proper field data is arguably the most critical aspect of designing an AI-based smart agricultural application, as this information can determine how well the final system will operate. Consequently, the employment of an AI-based analysis and prediction model will result in more accurate forecasts when it is able to harvest data from a much larger area of interest, taking into account a greater number of parameters, and having larger data sets. Furthermore, by harnessing the parallel processing ability of quantum computing, which is expected to deliver significantly increased processing speeds compared to traditional computing once fully developed, the time required for training and executing AI algorithms can be greatly reduced [74; 75; 119]. Several scientific studies have explored the use of quantum computing [120; 121] and quantum-assisted machine learning [122; 123] for various smart agriculture-related objectives. These objectives include optimizing data analysis and decision-making, specifically in areas such as production planning and insurance risk assessment, enhancing image processing techniques for disease diagnosis, as well as enhancing disease classification techniques. In addition, quantum computing enables the execution of complex simulations that can be used, for example, in forecasting and modeling weather patterns. Moreover, quantum-assisted machine learning techniques can be used to predict phenotypes from genomic data and facilitate drug discovery by identifying, validating, and characterizing biological targets that are effective against disease mechanisms.  
In addition, the widespread integration of blockchain technology and AI will permit the development of trustworthy and accurate prediction models. The combination of blockchain and quantum communication will eliminate the possibility of data manipulation by ensuring that critical data such as meteorological conditions, agricultural yields, and soil quality are securely delivered, maintained, and verified on the blockchain network. Consequently, the combination of AI, quantum communication, and blockchain will ensure that AI models are trained with data that are both reliable and immune to malicious tampering [63; 124]. Furthermore, with the development of 6G-IoT networks, computing power at the network's edge will increase considerably, enabling federated learning (FL), a technology that can exploit the increased processing capacity at the network's edge. FL enables the AI training data to remain locally, reducing the need to transmit sensitive information to the cloud. This strategy ensures that essential privacy and security concerns are addressed, as the data remain private and secure. Consequently, the use of FL in future 6G-IoT networks is expected to leverage the increased computing capacity at the edge while maintaining data privacy and security [60; 125].