A Comprehensive Review of Artificial Intelligence Applications in Precision Agriculture: Trends, Challenges, and Future Directions

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in modern agriculture, revolutionizing the way crops are monitored, pests are controlled, irrigation is managed, and diseases are diagnosed. This research synthesizes and critically evaluates ten recent scholarly works (2020–2025) focusing on AI applications in precision agriculture. These studies span across disciplines and technologies, including deep learning, Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), sensor networks, and edge computing. Collected research articles are assessed for its strengths, weaknesses, and identified research gaps. The analysis reveals a growing trend toward integrating AI with practical field tools and hardware systems, leading to increased precision and sustainability in agricultural operations. While some works provide conceptual frameworks and systematic reviews, others offer concrete deployments, such as AI-IoT pivot systems and ANN-based pest detection through wireless sensor networks. Common strengths include multimodal integration, sustainability orientation, and a strong push toward automation. However, significant gaps remain, including limited large-scale deployment, lack of economic feasibility studies, challenges in model interpretability, and insufficient adoption models for smallholder farmers. This review highlights the need for future research that bridges conceptual promise with field-level implementation, enabling AI to address complex, real-world agricultural challenges more effectively.