Work

Development of a wireless IoT sensor system as an alternative to the pour-through method for substrate fertility monitoring

Year: 2025

Type: article

Abstract: • A low-cost, solar-powered IoT system was developed to monitor container crop EC • Correlation established between in-field EC sensor readings and pour-through method • At low EC, two to four EC sensors needed to achieve pour-through accuracy Substrate electrical conductivity (EC) is widely used in nursery production to assess the nutrient content of the growing medium and guide fertilization management for container-grown plants. While several methods can be used to measure substrate EC, the process is often time-consuming and labor-intensive, making routine monitoring difficult to implement. New technologies are needed to help streamline the EC measurement and recordkeeping process. Recent advances in sensor technologies and increasing adoption of Internet of Things (IoT) tools in agriculture have enabled the development of automated, real-time monitoring systems for environmental parameters, including substrate EC. In this study, a low-cost IoT system was developed and deployed under typical nursery production conditions to monitor substrate EC in container-grown Lantana crops over a 10-week period. Manual EC measurements were also collected using the pour-through method to evaluate the relationship between sensor-based and conventional EC measurements under two irrigation methods (sprinkler and drip) and two fertilizer rates. Results of the comparison showed that sensor EC values were correlated with pour-through measurements under sprinkler irrigation, but no correlation was established under drip irrigation, likely due to uneven substrate saturation. Variation in the EC data was further analyzed to estimate the minimum number of sensors needed to achieve the same precision as the pour-through method. Results indicated that four sensors per 1000 pots are needed when using direct bulk EC measurements, and two sensors when using the Hilhorst method to approximate pore water EC. Additional sensors may be required to maintain this precision at higher EC levels.

Source: Smart Agricultural Technology

DOI: 10.1016/j.atech.2025.101663

Authors: Michelle Ezequelle, Kaiwen Xiao, Ying Zhang, Paul Fisher, Ana Martin-Ryals

Institution: University of Florida

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,770