Robot Identifies Plant Species Through Touch, Advancing Agricultural Research
Robot Identifies Plant Species Through Touch, Advancing Agricultural Research
Researchers in China have developed a robot capable of identifying plant species at various stages of growth by "touching" their leaves with an electrode. This robot can measure properties such as surface texture and water content, which current visual methods cannot detect. The study, published on November 13 in the journal Device, shows that the robot correctly identified ten different plant species with an average accuracy of 97.7% and achieved 100% accuracy in identifying leaves of the flowering bauhinia plant at multiple growth stages.
This technology could eventually assist large-scale farmers and agricultural researchers in monitoring crop health and growth. By using the robot, they could make more informed decisions about irrigation, fertilization, and pest control, according to Zhongqian Song, an associate professor at Shandong First Medical University & Shandong Academy of Medical Sciences and co-author of the study. He believes the robot could revolutionize crop management, ecosystem research, and early disease detection, all of which are essential for plant health and food security.
Unlike existing devices that rely on visual approaches, which are influenced by factors like lighting conditions and weather, the new robot uses tactile methods. Inspired by human skin, it employs a mechanism that "touches" the plant. When the robot's electrode makes contact with a leaf, it measures several properties: the charge that can be stored at a specific voltage, the electrical resistance of the leaf, and the contact force as the robot grips the plant.This data is then processed with machine learning algorithms, allowing the robot to classify the plant. Different values for each measurement correlate with specific plant species and growth stages.
Although the robot shows great potential for applications in precision agriculture, ecological research, and plant disease detection, there are still some challenges. The device is not yet capable of consistently identifying plants with complex structures, such as burrs or needle-like leaves. Song suggests that improving the design of the robot's electrode could resolve this issue.The researchers plan to expand the range of plants the robot can recognize by gathering data from more species, thus enhancing the plant species database for the algorithms. Additionally, they aim to further integrate the device's sensor to display results in real time, even without an external power source, making it more versatile for practical use.