Deep CNN-Based Intelligent Framework for Soil Moisture Prediction and Smart Irrigation Across Diverse Soil Types

Abstract

Efficient water management has become a critical requirement for sustainable agriculture due to increasing climatic variability, resource limitations, and the need for precision farming practices. Conventional irrigation approaches often rely on fixed schedules that fail to consider dynamic soil conditions, resulting in inefficient water utilization and reduced crop productivity. This research presents a Deep Convolutional Neural Network (Deep CNN)-based intelligent framework for soil moisture prediction and adaptive smart irrigation across diverse soil types. The proposed framework integrates soil moisture sensing, Internet of Things (IoT)-enabled monitoring, and deep learning-based prediction mechanisms to estimate moisture variations and support automated irrigation decisions. The model is designed to address variations in soil characteristics by learning complex relationships between sensor observations, environmental parameters, and moisture distribution patterns. Existing studies demonstrate the importance of accurate soil moisture monitoring, low-cost sensing technologies, IoT integration, and intelligent irrigation control mechanisms. However, limitations remain in achieving generalized prediction performance across heterogeneous soil environments. The proposed framework addresses this research gap by introducing a learning-driven architecture capable of adapting to multiple soil conditions. The study highlights the role of reliable data acquisition, feature optimization, and trustworthy artificial intelligence decision-making in agricultural automation. Human-centered AI reliability principles further emphasize the importance of transparency and confidence evaluation in intelligent agricultural systems (Ramamurthy et al., 2026). The findings suggest that Deep CNN-based irrigation intelligence can enhance water efficiency, reduce manual intervention, and contribute toward scalable precision agriculture solutions.