Xingang Liu

Papers

Total Citations

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About

Xingang Liu is a researcher at the forefront of agricultural automation, specializing in computer vision and deep learning for precision harvesting. His most-cited work, "Realization of Chrysanthemum Harvesting Recognition System based on CNN" (2022), tackles the critical challenge of enabling picking robots to accurately identify, classify, and position chrysanthemums under real-world conditions—such as variable lighting, occlusion, and plant posture. By leveraging convolutional neural networks (CNNs), Liu developed a robust image recognition system that overcomes these environmental complexities, directly advancing the viability of automated harvesting. This contribution, with 4 citations, lays essential groundwork for integrating AI into floriculture, reducing labor dependency and improving harvest efficiency. Liu’s research sits at the intersection of robotics, machine learning, and sustainable agriculture, offering practical solutions for smart farming. His work is particularly notable for addressing the nuanced visual recognition demands of non-uniform crops, a step toward fully autonomous agricultural systems. For students and researchers in agricultural engineering and computer vision, Liu’s studies provide a compelling model of how deep learning can be applied to real-world, unstructured environments.