Chinese scientists have created a cutting-edge robot capable of distinguishing between various plant species at multiple stages of growth, achieving this feat through the innovative use of electrodes to “touch” and analyze leaf structures. Researchers have developed a robotic system capable of measuring properties undetectable by conventional methods, such as floor texture and water content, according to a study published November 13 in the journal. The robot accurately identified ten distinct plant species, boasting a remarkable mean accuracy of 97.7%, as well as perfect recognition of Bauhinia leaves across various growth stages with 100% precision.
Ultimately, large-scale farmers and agricultural researchers might use the robotic to observe the well being and development of crops and to make tailor-made choices about how a lot water and fertilizer to provide their vegetation and find out how to method pest management, says Zhongqian Music, an affiliate professor on the Shandong First Medical College & Shandong Academy of Medical Sciences and an writer of the research.
“This breakthrough technology has the potential to revolutionise crop management, ecosystem research, and disease detection, thereby ensuring optimal plant health and food security.”
While it’s reasonable to prefer physical interaction with plants, modern technology can extract more detailed information through visual means, which however are vulnerable to factors such as varying light conditions, weather fluctuations, and background disturbances?
Researchers led by Music created a robot that “feels” vegetation using a mechanism inspired by human skin, with components working together in a hierarchical manner to gather data through touch. When an electrode on the robotic system comes into contact with a leaf, the machine acquires knowledge about the plant by measuring various properties: the current capacity at a given voltage, the resistance encountered as electrical current flows through the leaf, and contact dynamics as the robot grasps the leaf.
Subsequently, this data is processed using machine learning in order to categorize the plant, as distinct values for each measurement correspond with various plant species and stages of growth.
While robotic exhibits display vast, unforeseen capabilities across realms such as precision agriculture, ecological research, and plant disease detection, Dr. Music emphasizes that several limitations remain to be overcome. While the machine may struggle to identify varieties of vegetation featuring complex structures such as burrs or needle-like leaves? The issue may be alleviated by refining the robotic’s electrode design, he suggests.
“Accordingly, significant progress in both technological advancements and market trends is expected to precede the widespread adoption of innovative solutions.”
The researchers intend to augment the robotic’s capacity for recognizing vegetation by accumulating expertise from a broader range of flora, thereby expanding their plant species database to train algorithms. Researchers aim to integrate the machine’s sensors seamlessly, enabling real-time results without requiring an external power source, according to Music.
