Artificial Intelligence, Machine Learning, and IoT for Sustainable Agriculture: A Review of Smart Farming Technologies
Abstract
The increasing demand for food production, resource conservation, and climate-resilient agriculture has accelerated the adoption of Artificial Intelligence (AI), Machine Learning (ML), and Internet of Things (IoT) technologies in modern farming. Smart agriculture combines sensor networks, connected devices, satellite imagery, drones, and intelligent analytical models to support data-driven agricultural decision-making. This review examines recent developments in AI-, ML-, and IoT-enabled smart farming, with emphasis on crop disease detection, yield prediction, soil monitoring, irrigation optimization, pest management, weather forecasting, and precision agriculture. Various machine learning and deep learning techniques used to analyze agricultural data are reviewed, including convolutional neural networks, recurrent neural networks, ensemble learning, and reinforcement learning. The role of IoT sensors in collecting real-time information about soil moisture, temperature, humidity, nutrient levels, and crop conditions is also discussed. Furthermore, the review explores the use of edge computing, drones, remote sensing, and digital agriculture platforms for improving resource efficiency and agricultural productivity. Major challenges related to sensor reliability, connectivity, data availability, computational costs, interoperability, cybersecurity, and adoption by farmers are identified. The paper concludes by discussing future opportunities for autonomous farming, explainable AI, digital twins, and sustainable intelligent agricultural ecosystems.
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