Precision Agriculture: A Comprehensive Review of Technologies, Architectures, Deep-Learning Pipelines, and Future Prospects

Abstract

Artificial Intelligence is shaking up the way we grow food. Large farms already use AI-powered tools, such as deep learning, remote sensors, and IoT networks, to improve yield prediction, early disease detection, and more judicious resource use . Large-scale operations, about 60% of them, have improved their crop yields by 15–20%, while input costs have been cut down by a quarter. Smaller and mid-sized farms aren’t seeing the same advantages, though; only about 15% of them use these technologies, mostly because these are expensive, compli- cated, and hard to get up and running if the right infrastructure isn’t in place [9]. Research from 2020 to 2025 shows a number of factors to be considered about the technology itself, particularly the rise of deep learning models, hybrid CNN-LSTM set-ups, and transformer-based systems. It is not just a question of algorithms but how they function in real fields. Case studies from around the world show the biggest gains when the technology actually fits the local context, is transparent about how it works, and pulls in solid integrated data. In some areas, mostly temperate regions, yields of as high as 22% are realized. In intensively irrigated parts of South Asia, yields of a maximum of 30% can occur. These figures, however, are not always realized everywhere. A large number of schemes in the marginal areas of Sub-Saharan Africa never materialize due to technical support shortcomings, fluctuating funding, and inadequate farmer training. In parallel, there are researchers who extend the concept of explainable AI through incorporating heterogeneous data sources and directly deploying models on edge devices, hence no longer relying on cloud infrastructure. However, some limitations still exist, such as the difficulty in transferring models between regions, fragmented data governance, and several other barriers to user adoption for this technology. For AI-driven precision agriculture to realize its full potential, it needs to be affordable, transparent, and adaptable to local conditions to enable widespread adoption. Such attributes are central in advancing equal opportunities for large-scale and small-scale farming, supporting the deployment of smarter farming practices.