Plants cannot express themselves when they are thirsty. And visible signs, such as shriveling or browning leaves, do not appear until the majority of the water has been removed. Researchers from ACS Applied Materials & Interfaces created a wearable sensor for plant leaves to detect water loss earlier.
The system wirelessly transmits data to a smartphone app, allowing for remote drought stress management in gardens and crops. Newer wearable devices go beyond simple step counters. Some smartwatches now use electrodes against the skin to monitor the electrical activity of the wearer's heart. And, because many devices can wirelessly share collected data, physicians can monitor and assess their patient's health from afar.
Similarly, plant-wearable devices could assist farmers and gardeners in remotely monitoring the health of their plants, including leaf water content – a key indicator of metabolism and drought stress.
Previously, researchers developed metal electrodes for this purpose, but the electrodes had issues staying attached, reducing data accuracy. Renato Lima and colleagues wanted to find an electrode design that would be reliable for long-term monitoring of plant water stress while also remaining stationary.
The electrodes were made of two materials: nickel deposited in a narrow, squiggly pattern and partially burnt paper coated with a waxy film.
When the researchers used clear adhesive tape to attach both electrodes to detached soybean leaves, the nickel-based electrodes performed better, producing larger signals as the leaves dried out. The metal ones also stuck better in the wind, which was probably due to the thin squiggly design of the metallic film, which allowed more of the tape to connect with the leaf surface.
The researchers then used the metal electrodes to make a plant-wearable device that they attached to a living plant in a greenhouse. The device wirelessly transmitted data to a smartphone app and website, where it was successfully converted to the percentage of water content lost using a simple, fast machine learning technique.
According to the researchers, monitoring water content on leaves can provide information on pest and toxic agent exposure indirectly. They plan to test the plant-wearable device in outdoor gardens and crops to determine when plants need to be watered, potentially saving resources and increasing yields because the device provides reliable data indoors.
