Fig. 1: Impact of backbone depth on the classification performance of the Origin-Tea model.
From: Phenotypic feature-based identification of tea geographical origin using lightweight deep learning
The panels illustrate how varying the number of MBConv Blocks (from 0 to 9) within the optimal S01 configuration affects key evaluation metrics. The sub-figures correspond to: (a) Overall Accuracy (OA), (b) Precision, (c) F1-score, (d) Average Accuracy (AA), (e) Recall, (f) Kappa Coefficient, and (g) Matthews Correlation Coefficient (MCC). The solid lines represent the mean performance scores averaged across the 10-fold cross-validation, while the shaded regions indicate the standard deviation, reflecting the stability of the model. The trends demonstrate that a shallow architecture (Block depth = 0) achieves the highest stability and accuracy, whereas increasing depth leads to performance fluctuations and diminishing returns.
