# Development of a 3D-Printed Capacitive Sensor for Soil Water Content Estimation Using Nickel-Based Conductive Paint

## TL;DR

A low-cost 3D-printed capacitive sensor was developed to accurately estimate soil water content, offering a reliable and affordable alternative to existing methods.

## Contribution

The novel use of 3D printing and nickel-based conductive paint to create a cost-effective capacitive sensor for soil water content estimation.

## Key findings

- The sensor demonstrated consistent and reliable performance within a soil water content range of 0 to 0.40 cm³/cm³.
- The device's performance was validated against the reference TDR method and showed acceptable accuracy.
- The sensor is suitable for distributed monitoring and IoT-based environmental applications.

## Abstract

What are the main findings?
Development of a low-cost capacitive sensor.Consistent and reliable performance.

Development of a low-cost capacitive sensor.

Consistent and reliable performance.

What are the implications of the main findings?
It is possible to build the device on one’s own.The device is suitable for monitoring soil water content with acceptable accuracy.

It is possible to build the device on one’s own.

The device is suitable for monitoring soil water content with acceptable accuracy.

Understanding hydrological, agricultural, and environmental processes in soils relies on accurately measuring volumetric water content (θ), matric potential (h), and hydraulic conductivity (K). These parameters are fundamental for quantifying plant-available water, optimizing irrigation scheduling in precision agriculture, modeling watershed responses, and studying the impacts of climate change in complex ecosystems. Among these parameters, θ is truly indispensable, as it represents the primary indicator of the water status of soils and a prerequisite for interpreting the other hydraulic variables. In recent years, capacitive sensors have become one of the most widely adopted technologies for θ estimation, owing to their favorable balance between accuracy, robustness, and affordability. These sensors infer soil moisture by measuring dielectric permittivity of soils, which is strongly governed by water content, making them particularly suitable for distributed monitoring and IoT-based environmental applications. The present study aimed to develop a low-cost capacitive sensor for θ estimation. This sensor can be made using 3D printing technology combined with conductive, nickel-based paint, which (once applied on the 3D-printed guides) forms the capacitive electrode. The capacitive component operates at an operational frequency of 60 MHz. The system was subjected to a rigorous testing protocol, including calibration and validation phases under laboratory conditions using three soils of different textures. Its performance was specifically compared with the time-domain reflectometry (TDR) technique, which is widely recognized in Soil Physics and Soil Hydrology as the reference method for θ estimation due to its reliability and accuracy. These tests confirmed the effective performance of the proposed sensor, which overall exhibited good reliability within the selected validation range, corresponding to a θ range of 0 to 0.40 cm3/cm3.