Self-calibrating neuromorphic system for adaptive environmental sensing

Precision agriculture demands accurate, real-time environmental monitoring, conventional soil moisture sensors face critical issues such as long-term drift, high energy consumption, and limited adaptability to dynamic environmental changes. These limitations often lead to suboptimal irrigation decisions, wasted resources, and unreliable data, especially in remote or resource-constrained farming regions where frequent manual recalibration is impractical or impossible. This work addresses these challenges by introducing a novel self-calibrating neuromorphic system for adaptive soil moisture sensing. The system leverages Spiking Neural Networks (SNN) deployed on a low-power STM32H563ZI microcontroller. Our proposed solution autonomously recalibrates sensors to mitigate drift, significantly reduces energy consumption through event-driven computation, and adapts seamlessly to changing environmental conditions. The SNN model achieved a Mean Absolute Error (MAE) of 0.4557 and a Root Mean Squared Error (RMSE) of 0.5850, reducing baseline drift from 5.3% to 1.6% over a two-month deployment outperforming models like Isolation Forests and Autoencoders in predictive accuracy. This work significantly contributes to the growing field of neuromorphic computing in IoT applications, offering a scalable, low-power solution for precision agriculture and broader environmental monitoring. The demonstrated effective deployment of SNN-based learning mechanisms on low-constrained microcontroller hardware opens new avenues for resilient, decentralized intelligence in smart homes, wearables, and autonomous infrastructure inspection.