Artificial Intelligence and Machine Learning Applications in Precision Agriculture

Precision agriculture is quickly revolutionizing the farming sector with the incorporation of emerging technologies including Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and remote sensing. This paper synthesizes and reviews evidence from 24 recent publications that investigate the application and effects of the above technologies in precision farming. The literature surveyed reflects tremendous improvement in crop monitoring, yield estimation, pest identification, irrigation scheduling, and autonomous decision-making. AI and ML models, such as deep learning, are used successfully to analyze sophisticated agronomic data from sensors, drones, and satellite images. The importance of AIoT in real-time data processing and intelligent resource management is also highlighted. In addition, the integration of 5G-capable IoT devices and AI algorithms is demonstrated to improve data transmission and predictive quality for field applications. While the advantages involve increased productivity, resource effectiveness, and sustainability, various studies point out challenges such as excessive implementation cost, low farmer awareness, and privacy in data. The review concludes that the union of AI and ML with precision agriculture is instrumental for attaining sustainable and resilient farming systems worldwide, particularly under evolving climatic conditions and increasing food needs.