Table 5. Performance evaluation of HierbaNetV1 against pre-trained models on SorghumWeedDataset_Classification using accuracy, precision, recall, F1-score, and loss.

Model	Accuracya	Precision	Recall	F1-score	Loss
HierbaNetV1 (Proposed model)	0.9861	0.9860	0.9862	0.9860	0.0700
Pre-trained models
InceptionV3	0.9791	0.9795	0.9795	0.9792	1.5472
VGG19	0.9698	0.9704	0.9702	0.9698	0.1444
ResNet152V2	0.9675	0.9685	0.9681	0.9676	1.4649
DenseNet201	0.9582	0.9601	0.9590	0.9583	1.0096
MobileNetV2	0.9327	0.9336	0.9321	0.9323	2.2053
SOTA architectures
Hybrid CNN-Transformer	0.9606	0.9604	0.9605	0.9605	1.1503
DarkNet53	0.9397	0.9395	0.9397	0.9395	2.5037
RCNN	0.9142	0.9140	0.9142	0.9140	3.0356

Note:

^aThe table is organized based on accuracy within each section.