Table 7.

Performance comparison of LCAMNet and similar crop disease image classification models on the SCEBD.

Models	Accuracy (%)	Recall (%)	Precision (%)	F1 (%)	Kappa (%)	MCC (%)	GFLOPs (G)	Parameters (M)
Re-GoogLeNet	93.37	93.36	93.79	93.46	92.42	92.64	2.80	9.11
ALS-Net	91.07	91.09	91.28	91.13	89.80	89.81	0.73	1.18
LBMRNet	90.05	90.06	91.54	90.30	88.63	88.81	0.17	0.91
LCAMNet	92.60	92.64	92.85	92.64	91.55	91.58	0.03	1.30

Bold values indicate the best results in each column. For Accuracy, Recall, Precision, F1, Kappa, and Matthews Correlation Coefficient (MCC), higher values indicate better performance. Conversely, for GFLOPs and Parameters, lower values indicate a more lightweight and efficient model.