Weiqing Min

Papers

Total Citations

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About

Weiqing Min is a leading researcher in computer vision and deep learning, with a particular focus on agricultural AI and fine-grained visual recognition. His most-cited work introduces a multi-scale attention convolutional neural network (CNN) for vision-based fruit recognition, a contribution that has garnered 40 citations since its 2023 publication. This paper exemplifies his broader impact: developing efficient, attention-driven architectures that enhance the accuracy and robustness of object recognition in complex, real-world environments—especially for agricultural applications like automated harvesting and quality assessment. Min’s research bridges the gap between advanced deep learning models and practical, domain-specific tasks, making his work highly influential in both the computer vision and precision agriculture communities. His contributions are notable for their emphasis on multi-scale feature extraction and attention mechanisms, which have become foundational in tackling challenges such as occlusions, varying lighting, and morphological diversity in natural scenes. With a growing citation record, Weiqing Min is establishing himself as a key figure in the intersection of artificial intelligence and agriculture, inspiring further innovation in smart farming and visual recognition systems.