Artificial-Intelligence-Enhanced Virtual Sensor System for Smart Farming: Modeling Ancestral Cultivation Practices in Simulink

This study presents a validated AI-based data acquisition system for precision agriculture, fully modeled in MATLAB/Simulink R2025a. The system integrates virtual sensors, convolutional neural networks (CNNs), and image-based root analysis to support the ancestral “Huacho Rosado” potato cultivation technique. It is structured into three layers: Environmental Data Acquisition, AI-driven processing, and agronomic Decision Support. Virtual sensors simulate soil temperature, moisture, and density, while CNN modules classify soil texture, estimate moisture, and detect root density using RGB images. The decision-support layer computes agronomic forces—bit, shear, and inertial—which are essential for soil management. Simulation results demonstrate real-time inference below 200 ms, moisture prediction errors under 5%, and root density classification accuracy of 90%. A continuous 24-h simulation confirmed the system’s stability and responsiveness. This approach provides a low-cost, scalable, and reproducible framework that bridges indigenous knowledge with modern AI tools, supporting sustainable agriculture in resource-limited environments.