AI-DRIVEN FARMING: MACHINE VISION, PREDICTIVE AGRICULTURE, AUTONOMOUS FARMING, AND SMART HARVESTING

The rapid integration of artificial intelligence into agriculture is transforming farming from a largely reactive production system into a data-driven, predictive, and increasingly autonomous environment. This article examines four interconnected technological directions that define AI-driven farming: machine vision, predictive agriculture, autonomous farming, and smart harvesting. The study synthesizes recent international and Uzbek research published within the last decade and evaluates how computer vision, machine learning, sensor networks, robotics, and intelligent decision-support systems can improve crop monitoring, resource allocation, field operations, and harvesting quality. Particular attention is given to the role of RGB, multispectral, thermal, depth, and three-dimensional imaging in crop recognition and localization; the use of predictive models for yield, irrigation, disease, and operational planning; the architecture of autonomous agricultural vehicles and robots; and the technical challenges of robotic harvesting in unstructured environments. The analysis shows that the greatest value of artificial intelligence is achieved not through isolated algorithms but through integrated systems that connect perception, prediction, decision-making, and actuation. For Uzbekistan, such integration is especially relevant to irrigated agriculture, horticulture, greenhouse production, and water-efficient farming. The article concludes that future progress depends on locally representative datasets, reliable field validation, affordable sensing and robotic platforms, interoperability, and the development of human capital capable of operating and maintaining intelligent agricultural systems.