Abstract: Precision Agriculture is a combination of Artificial Intelligence (AI) and the Internet of Things (IoT) to improve farming efficiency, sustainability, and overall productivity. This work presents hybrid CNN-TAM (Convolutional Neural Network–Temporal Attention Mechanism) model running on Edge AI devices for real time crop soil temperature and Soil Moisture prognosis. IoT sensors gather long term environmental data which is preprocessed to remove noise and extract meaningful spatial and temporal features. CNN can obtain spatial patterns and TAM assigns dynamic attention weights to important time steps enhancing prediction accuracy. The proposed hybrid model surpasses the conventional methods like Linear Regression, Random Forest, LSTM, and independent CNN with the lowest RMSE (1.7). Different from cloud-based deployments, the Edge AI deployment offers reduced latency, consumes lower bandwidth, and is better suited for scalability, enabling large-scale, real-time precision farming. Experimental outcome confirms enhanced real-time prediction capability allowing farmers to optimize irrigation schedules, reduce resource waste, and improve crop resilience against extreme weather conditions. This ensures sustainable resource management, conserves water and fertilizers, and enhances decision-making in agriculture. The results demonstrate the capability of AI-driven decision-support tools in present-day agriculture and presents a scalable, cost-effective and deployable solution for both small- and large-scale farms. By emphasizing data privacy, real-time processing, and low-latency inference, this research contributes to the area of precision agriculture relying on AI, addressing key challenges such as real-time analytics, unreliable connectivity, and the need for immediate on-site decision-making. The study develops an AI-powered system for intelligent farm management to support sustainable and Smart Irrigation Optimization is used for efficient agricultural practices.

M. L. Suresh, Swaroopa Rani B, T K Rama Krishna Rao, S. Gokilamani, Yousef A.Baker El-Ebiary, Prajakta Waghe and Jihane Ben Slimane, “A Hybrid Convolutional Neural Network-Temporal Attention Mechanism Approach for Real-Time Prediction of Soil Moisture and Temperature in Precision Agriculture” International Journal of Advanced Computer Science and Applications(IJACSA), 16(5), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160556

@article{Suresh2025,
title = {A Hybrid Convolutional Neural Network-Temporal Attention Mechanism Approach for Real-Time Prediction of Soil Moisture and Temperature in Precision Agriculture},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160556},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160556},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {5},
author = {M. L. Suresh and Swaroopa Rani B and T K Rama Krishna Rao and S. Gokilamani and Yousef A.Baker El-Ebiary and Prajakta Waghe and Jihane Ben Slimane}
}