Transforming agriculture with artificial intelligence: A scoping review of technologies and trends

Ako Kakarash Omer; Ayas Hussein; Safar Maghdid Assad

doi:10.5455/AAS.20260304090202

Abstract
AI is becoming increasingly popular and can be applied across various agricultural operations. This study examines recent academic publications from 2017 to 2025, assessing the transformative potential of AI and associated technologies and their applicability to contemporary agricultural methods. Deep learning (DL), machine learning (ML), convolutional neural networks (CNNs), and computer vision (CV) are among the many AI technologies that are excellent for agriculture. The review included a summary of the findings from a remarkable number of peer-reviewed sites, including Springer, Elsevier, MDPI, ScienceDirect, and Google Scholar. It maximizes agricultural resource use by facilitating access to precision farming, crop surveillance, and mechanical automation. This study examines recent academic publications evaluating the revolutionary potential of AI and related technologies and their current use in agricultural operations. The study concludes with recommendations for future directions that include increased cross-sector information sharing, edge computing deployment, and AI system integration because the fair, sustainable, and effective AI deployment in agriculture depends on those algorithms.

Key words: Artificial Intelligence, Deep Learning, Convolutional Neural Network, Transforming Agriculture, Machine Learning and Digital Transformation