Jianwen Tan

Papers

Total Citations

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About

Jianwen Tan is a researcher at the forefront of agricultural robotics and intelligent automation, with a focus on computer vision and motion planning. Tan’s work addresses critical challenges in precision agriculture, particularly through innovative deep learning techniques. A standout contribution is the development of an improved DeepLabV3+ model for segmenting strawberries at varying maturity levels, which tackles the persistent problem of fruit adhesion and stacking in image analysis. This method, enhanced with attention mechanisms, achieved 28 citations and offers a robust solution for automated harvesting systems. In parallel, Tan advanced robotic manipulation by proposing an improved RRT* algorithm for robotic arm path planning, reducing both planning time and path distance—a key step toward efficient, real-time automation in agricultural settings, with 13 citations. These contributions demonstrate Tan’s ability to bridge computer vision and robotics, delivering practical tools for smart farming. With a growing body of work that integrates state-of-the-art AI with real-world agricultural needs, Tan is shaping the future of autonomous crop management and robotic efficiency.