Low-Cost Sensor Detects Crop Salt Stress Early

By Blake Jackson

Soil salinity remains a major agricultural challenge, occurring when excess soluble salts limit a plant’s ability to absorb water.

According to the U.S. Department of Agriculture, this issue affects nearly 30% of irrigated farmland in the United States, reducing crop productivity and farm profitability.

Often caused by irrigation practices, inadequate drainage or saltwater intrusion, salinity can degrade soil structure and fertility over time.

To address this concern, a research team led by Penn State scientists has developed an affordable sensor system designed to detect early signs of salt stress in crops.

The system identifies volatile organic compounds (VOCs), gases naturally released by plants, which change when plants are under stress.

“The low-cost sensor system we developed detects volatile organic compounds released by plants when stressed - think of it like an electronic nose for crops that ‘smells’ gases put off by plants in distress and can warn farmers of salt stress early, before visible damage occurs,” said co-author Francesco Di Gioia, Penn State associate professor of vegetable crop science.

“Salinity stress is a major issue in many regions and coastal areas around the world, and most vegetable crops are highly susceptible to the accumulation of salts like sodium chloride, which hinder nutrient uptake and decrease productivity.”

The study was led by Ali Ahmad, a doctoral researcher from the Polytechnic University of Valencia, who conducted the work at Penn State.

Using arugula grown in a controlled hydroponic greenhouse, researchers introduced varying salt levels to create different stress conditions.

“We used a hydroponic system for the experiment to be able to control the level of salinity and exclude other factors, to be sure that what we were detecting on the plants' volatile profile was determined by the difference in salinity levels,” Ahmad said.

Low-cost metal-oxide semiconductor sensors monitored the gases over eight days, identifying distinct patterns linked to stress levels. Researchers then applied machine learning models to classify these signals.

“We studied metal-oxide semiconductor sensors because they are small and easy to deploy, widely available online and very cheap - some under $1,” Ahmad said.

“That means farmers could potentially deploy many sensors across a field. But before they could become a major tool in precision agriculture, technical improvements are needed in sensor hardware and networks.”

The system achieved up to 99.15% accuracy in detecting stress, confirmed by visible plant changes.

Photo Credit: pexels-greta-hoffman