Enhancing Sensors Accuracy for Deploying Precision Agriculture in the Field

Sensor drift, caused by environmental and operational stresses, is a significant issue that impacts several domains. In agriculture, it is particularly relevant during irrigation, fertilization, and climate monitoring, ultimately affecting crop yield and quality. This paper adopts a deep edge-centric approach, minimizing reliance on cloud and non-deep edge platforms to improve data privacy, reduce latency and power consumption, and support real-time decision-making. This strategy involves designing lightweight, resource-efficient algorithms that operate on energy -constrained and low -cost deep edge devices, such as the sensors which are essential components to serve metrology purposes. The work is focused on devising on-(ultra tiny)device learning techniques, enabling continuous adaptation to changing conditions while meeting strict power consumption requirements such as the sensor's one. A key contribution is the TinyRBF model, a deeply constrained dynamic Radial Basis Function network tailored for edge devices. TinyRBF addresses the limitations of existing on-device learning methods by balancing prediction accuracy with memory and computational efficiency. Experimental results collected in a challenging agricultural testbed demonstrate its low resource usage and robust performance, making it a scalable solution for real-world applications.