Low-Cost “Electronic Nose” Technology Helps Farmers Detect Crop Salt Stress Early and Protect Yields.
Soil salinity can pose a serious threat to crops, as excess salts in the soil restrict a plant’s ability to take up water. The U.S. Department of Agriculture estimates that nearly 30% of irrigated farmland across the United States is impacted, leading to reduced yields and lower farm profitability.
Factors such as improper irrigation, poor drainage, and saltwater intrusion often contribute to this issue, gradually damaging soil health and structure.
To help tackle this problem, a team of researchers at Penn State has introduced a cost-effective sensor system capable of identifying early signs of salt stress in crops.
The technology works by detecting volatile organic compounds (VOCs), which are gases naturally emitted by plants. These emissions shift when plants experience stress, offering an early warning signal.
“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, led by doctoral researcher Ali Ahmad from the Polytechnic University of Valencia in collaboration with Penn State, involved growing arugula in a hydroponic greenhouse environment with controlled salt levels.
“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.
Using low-cost metal-oxide semiconductor sensors, some priced under $1, researchers monitored plant emissions over eight days.
Combined with machine learning models, the system identified stress patterns with an accuracy of up to 99.15%, highlighting its potential for precision agriculture.
Photo Credit: penn-state