Abstract

Long-term deployment of electrochemical sensors in agricultural environments is often limited by dehydration of the sensing interface, accumulation of contaminants, and gradual degradation of sensor performance. These challenges can reduce measurement reliability and require frequent manual maintenance, making large-scale and remote deployments difficult. To address these limitations, this research presents the design, development, and validation of an automated fluidic maintenance platform capable of maintaining sensor hydration and recovering sensor functionality during extended field operation. The proposed system integrates an Arduino MKR WiFi 1010 controller, peristaltic pumps, solenoid valves, fluid reservoirs, and a programmable control algorithm to perform automated maintenance procedures. The maintenance strategy consists of a daily DI-water washing sequence and weekly regeneration routines designed to restore degraded sensor responses. The developed three-sensor automated maintenance platform was deployed across 14 systems in Canal Point, Florida, and 8 systems in Houma, Louisiana, where its performance was evaluated under real agricultural field conditions. Experimental results demonstrated that automated washing successfully restored the characteristic impedance response associated with proper sensor hydration, while automated regeneration recovered sensor functionality following long-term degradation. Consistent recovery was observed across all three sensing channels, validating the effectiveness of the fluidic maintenance architecture and control strategy. The platform operated autonomously and reduced the need for manual intervention during deployment. The results demonstrate that the proposed maintenance platform provides an effective 5 solution for preserving electrochemical sensor performance during long-term operation. The developed system contributes to the advancement of autonomous agricultural sensing technologies by improving sensor reliability, extending operational lifetime, and supporting scalable multi-sensor deployments.

Date of publication

7-2026

Document Type

Thesis

Language

english

Persistent identifier

http://hdl.handle.net/10950/5120

Committee members

Dr. Shawana Tabassum, Dr. Premananda Indic and Dr. Md Masud Rana

Degree

MS in Electrical Engineering

Available for download on Thursday, July 27, 2028

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