This study proposes DGLNet, a novel lightweight and highly accurate network for rice disease identification.
This study proposes DGLNet, a novel lightweight and highly accurate network for rice disease identification.
A lightweight rice disease identification network based on ...
ui.adsabs.harvard.edu › abs › abstract
The GAM is designed to capture key information in complex noisy scenes, thus improving the generalization ability of the model. Meanwhile, the self-developed ...
A lightweight rice disease identification network based on attention mechanism and dynamic convolution. https://doi.org/10.1016/j.ecoinf.2023.102320 ...
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This paper proposes a rice disease identification method based on an improved DenseNet network (DenseNet).
Nov 1, 2023 · By introducing the attention mechanism into a model, it can better understand the correlation between the input data, and improve its prediction ...
Jun 19, 2024 · This review paper addresses the critical need for advanced rice disease detection methods by integrating artificial intelligence, ...
Jan 11, 2024 · (2023) introduced DGLNet, a rice disease diagnosis network that is both lightweight and accurate. The Global Attention Module (GAM) and Dynamic ...
Jan 4, 2023 · This paper proposes a rice disease identification method based on an improved DenseNet network (DenseNet). This method uses DenseNet as the ...
A lightweight rice disease identification network based on attention mechanism and dynamic convolution. Ecological Informatics 78:102320. doi: 10.1016/j ...