Artificial Intelligence-Driven Precision Agriculture Using Computer Vision and IoT
Keywords:
Artificial Intelligence, Precision Agriculture, Computer Vision, Internet of ThingsAbstract
The Agriculture is experiencing a fundamental transformation through the convergence of Artificial Intelligence (AI), Computer Vision (CV), and the Internet of Things (IoT), enabling the development of precision agriculture systems capable of improving productivity, sustainability, and resource efficiency. Conventional agricultural practices often rely on generalized cultivation methods, leading to inefficient utilization of water, fertilizers, pesticides, and labor while limiting the ability to respond dynamically to environmental variability. Artificial Intelligence-driven precision agriculture addresses these limitations by integrating intelligent sensing, automated image analysis, real-time environmental monitoring, and predictive analytics into unified decision-support frameworks. Computer vision technologies facilitate automated crop monitoring, disease diagnosis, weed identification, fruit maturity assessment, and yield estimation through image-based analysis, whereas IoT infrastructures continuously collect multidimensional environmental data including soil moisture, temperature, humidity, nutrient concentration, and weather conditions. AI algorithms synthesize these heterogeneous data sources to optimize irrigation scheduling, fertilization strategies, pest management, and harvesting operations. Recent advances in deep learning, edge intelligence, cloud computing, digital twins, sensor fusion, and intelligent automation further enhance the scalability and operational efficiency of smart farming ecosystems (Hussain et al., 2026; Philip, 2025). This review synthesizes recent developments in AI-enabled precision agriculture by examining the integration of computer vision and IoT technologies, evaluating architectural frameworks, intelligent analytics, implementation challenges, and future research opportunities. The study also proposes a conceptual integrated framework that combines intelligent sensing, machine learning, cloud-edge collaboration, and autonomous decision-making for sustainable agricultural production.
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