Tohoku University Develops Leaf-Mounted Sensor to Monitor Plant Health

Image Credits:TOHOKU UNIVERSITY

Researchers at Tohoku University have developed a new leaf-mounted sensor designed to help monitor plant health and improve agricultural productivity. The small, leaf-mounted device tracks changes in leaf color to detect plant stress, offering a more efficient and accessible way to manage crops and resources.

Addressing the Impact of Climate Change on Plants

As climate change leads to more frequent extreme weather events such as heatwaves, heavy rain and droughts, plants face increased stress, which can reduce crop yields and threaten the health of ecosystems. Monitoring how plants respond to these changes is crucial to sustaining farm and forest productivity. Traditional methods, such as using drones or aircraft to monitor plants, provide surface-level data and struggle to track individual plants over time. Additionally, manual checks of small sensors placed on plants are time-consuming and require on-site personnel.

Kaori Kohzuma, one of the researchers, explains the challenges with current methods: “The traditional methods may work well for some purposes, but they are difficult to operate and quite expensive. In order to continuously monitor small changes, we needed a new solution.”

A Leaf-Mounted Sensor for Continuous Monitoring

To solve these issues, the research team created a new leaf-mounted sensor that attaches to the underside of plant leaves. This device uses a spectroscopic sensor and light source to measure changes in leaf color without blocking sunlight, ensuring that plants receive the light they need while still capturing accurate data. The leaf-mounted sensor can track changes in the same location over time, allowing for continuous monitoring of plant health. It is powered by a battery and features Wi-Fi data transfer and waterproofing, making it suitable for outdoor use for over a month.

Spectral sensors, also called spectrometers are instruments that are used in spectroscopy to study light. Spectral sensors capture and measure the light reflected or emitted by an object or scene in the form of a reflectance spectrum.

Observations of birch leaves in the autumn foliage stage over a two-week period. (Top) Time-course changes across eight wavelengths. (Middle) Variation in sunlight intensity. (Bottom) Actual changes in leaf color. As the green color shifts to yellow and brown, changes in reflectance can be observed.

Sensor Performance and Testing

In trials, the new leaf-mounted sensor was tested on about 90 leaves from 30 different plant species. It performed well in distinguishing color changes across seven out of eight detectable wavelengths, with readings at 620 nanometer (nm) closely matching those of commercial chlorophyll meters. Further tests with a stress-sensitive Arabidopsis thaliana mutant showed that changes at 550 nm in the sensor’s readings corresponded with the plant’s stress responses, which matched the widely used Photochemical Reflectance Index (PRI).

Real-World Trial for Leaf Monitoring

To test the leaf-mounted sensor in real-world conditions, the team attached it to birch leaves during autumn to track changes in leaf color over two weeks. The sensor detected a decrease in chlorophyll levels, a key indicator of plant stress and observed how these changes varied with sunlight intensity.

Also read: Robot Identifies Plant Species Using Electrode Touch Technology

Kohzuma highlights the affordability of the leaf-mounted sensor, noting its potential to support large-scale monitoring efforts.

“This affordable sensor is a promising tool for accurately monitoring plant health and stress through leaf color and light reflection data. Its low cost makes it possible to place multiple sensors across various locations, creating a network for simultaneous monitoring in many spots.”
Kaori Kohzuma, Researcher, Tohoku University

Potential Applications in Agriculture and Beyond

This new technology has the potential to be widely used in smart farming, forest health studies and any area requiring detailed tracking of plant health. The ability to provide real-time, precise data on plant stress and health can help farmers and researchers respond more quickly to changes in plant conditions, ultimately supporting better resource management and crop yield optimization.

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