The increasing demand for sustainable and efficient agricultural practices has necessitated the adoption of advanced computing technologies. While cloud and fog computing have been widely utilized in smart agriculture, they exhibit significant limitations such as high latency, energy inefficiency, scalability issues, and elevated costs. To address these challenges, this paper proposes a novel Dew Computing Framework tailored for the agriculture sector. The framework leverages localized data processing at the dew layer to optimize key performance metrics, including latency, energy consumption, scalability, cost, and reliability. The proposed framework is evaluated against traditional cloud and fog computing methodologies across five configurations using real-world and synthetic agricultural datasets. Experimental results demonstrate that the proposed framework consistently outperforms existing techniques, achieving substantial reductions in latency and energy consumption while improving data reliability and scalability. Furthermore, the decentralized nature of the framework ensures seamless integration of IoT devices, making it highly adaptable for diverse agricultural environments. This study contributes to the advancement of precision agriculture by enabling real-time decision-making and resource optimization through an efficient and cost-effective computing approach. The findings highlight the potential of dew computing to revolutionize smart agriculture and pave the way for further integration of emerging technologies, such as machine learning and blockchain, into agricultural systems. Future work aims to extend the framework’s capabilities and validate its performance in large-scale agricultural deployments.