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About

Shumao Wang is a leading researcher in agricultural robotics and intelligent harvesting systems, with a focus on machine vision, autonomous navigation, and crop–machine interaction. His work addresses critical bottlenecks in automated fruit harvesting—particularly the challenges of speed, accuracy, and fruit damage in unstructured environments. Wang’s highly cited studies include the design of a four-wheel independent steering and control system for agricultural wheeled robots (12 citations) and an investigation into bruise responses from apple-to-apple impacts (12 citations), which provides foundational data for reducing mechanical damage during bulk harvesting. He also developed a walking goal line detection algorithm based on the Hough transform for combine harvesters (3 citations), advancing aided driving in field operations. More recently, Wang proposed a BlendMask-BiFPN-based method for detecting the relative position of clustered tomato bunches (2 citations, 2024), enabling manipulators to navigate around obstacles with greater precision. His research integrates computer vision, deep learning, and mechatronic control to make robotic harvesting more robust and commercially viable. Wang’s contributions are essential for students and engineers working toward practical, damage-sensitive automation in specialty crop production.