Abstract
Perception of garden environment plays a crucial role in the control of invasive plants. Timely and accurate detection of invasive plants can significantly enhance the effectiveness of their control measures. Neural network technology can effectively identify the presence of invasive plants in the environment so that effective measures can be taken to protect garden plants from invasive species. In this paper, we propose a deep bilinear transformed ResNeXt (DBT-ResNeXt) model that replaces ResNet with ResNeXt to construct a two-branch structure for invasive plant recognition. The model is trained and validated using a dataset constructed from publicly available images of invasive plants. Experimental results demonstrate that compared to various other models, DBT-ResNeXt exhibits superior performance, achieving a recognition accuracy of 94.66%.
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