Xuchen Li
Papers
3
Total Citations
107
H-Index
3
About
Xuchen Li is a researcher at the forefront of applying deep learning to agricultural quality assessment, with a particular focus on fruit appearance grading. His work centers on developing novel computer vision architectures that combine state-of-the-art neural networks with traditional machine learning classifiers to solve real-world agricultural challenges. Li's most impactful contribution is the Swin-MLP method, which integrates Swin Transformer with multi-layer perceptron for strawberry quality identification—a paper that has garnered 74 citations since 2022. He further advanced the field with YOLOX-Dense-CT, a detection algorithm for cherry tomatoes that achieved 22 citations, and ResNeXt-SVM, which innovatively fuses ResNeXt network features with support vector machines for enhanced strawberry grading. Collectively, his work demonstrates a consistent theme: bridging advanced transformer-based and convolutional neural network architectures with practical agricultural applications. Li's research is notable for its direct industry relevance, offering scalable solutions for automated fruit sorting and quality control. His publications, though recent, have already established him as a rising voice in precision agriculture, with his methods being cited by researchers working on similar fruit detection and classification problems worldwide.
Research Focus
Key Achievements
Top Papers
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