Robot identifies plants by “touching” their leaves
By Jill Dando News
Researchers in China have developed a robot that identifies different plant species at various stages of growth by “touching” their leaves with an electrode.
The robot can measure properties such as surface texture and water content that cannot be determined using existing visual approaches, according to the study, published November 13 in the Cell Press journal Device.
The robot identified ten different plant species with an average accuracy of 97.7% and identified leaves of the flowering bauhinia plant with 100% accuracy at various growth stages.
Eventually, large-scale farmers and agricultural researchers could use the robot to monitor the health and growth of crops and to make tailored decisions about how much water and fertilizer to give their plants and how to approach pest control, says Zhongqian Song, an associate professor at the Shandong First Medical University & Shandong Academy of Medical Sciences and an author of the study.
“It could revolutionize crop management and ecosystem studies and enable early disease detection, which is crucial for plant health and food security,” he says.
Rather than making physical contact with a plant, existing devices capture more limited information using visual approaches, which are vulnerable to factors such as lighting conditions, changes in the weather, or background interference.
To overcome these limitations, Song and colleagues developed a robot that “touches” plants using a mechanism inspired by human skin, with structures working together in a hierarchical way to gain information through touch.
When an electrode in the robot makes contact with a leaf, the device learns about the plant by measuring several properties: the amount of charge that can be stored at a given voltage, how difficult it is for electrical current to move through the leaf, and contact force as the robot grips the leaf.
Next, this data is processed using machine learning in order to classify the plant, since different values for each measure correlate with different plant species and stages of growth.
While the robot shows “vast and unexpected” potential applications in fields ranging from precision agriculture to ecological studies to plant disease detection, it has several weaknesses that have yet to be addressed, says Song.
For example, the device is not yet versatile enough to consistently identify types of plants with complicated structures, such as burrs and needle-like leaves. This could be remedied by improving the design of the robot’s electrode, he says.
“It may take a relatively long period of time to reach large-scale production and deployment depending on technological and market developments,” says Song.
As a next step the researchers plan to expand how many plants the robot can recognize by collecting data from a wider variety of species, boosting the plant species database they use to train algorithms.
The researchers also hope to further integrate the device’s sensor so that it can display results in real time, even without an external power source, says Song.
