HairNet: a deep learning model to score leaf hairiness, a key phenotype for cotton fibre yield, value and insect resistance

Table 4 Effect of a range of data augmentation techniques on the % accuracy of our three best model architectures, ResNet18, ResNet34 and ResNet101, trained on the whole dataset

Data augmentation \(\downarrow \)	ResNet18			ResNet34			ResNet101
Data augmentation \(\downarrow \)	IA	FIA	LA	IA	FIA	LA	IA	FIA	LA
Baseline	83.19	78.41	89.56	84.85	81.65	91.36	82.49	83.09	88.12
RV Flip	87.98	84.17	92.08	88.65	87.56	92.44	85.96	85.25	90.28
RH Flip	87.87	84.53	92.80	88.98	87.12	91.72	84.63	81.65	90.64
RV + RH Flip	88.43	85.97	93.88	89.20	86.69	92.80	87.80	85.97	93.52
RC	82.32	80.93	88.48	83.67	81.65	87.05	82.38	83.09	89.20
RC + RV Flip	83.78	83.09	87.76	86.25	84.17	93.16	83.60	82.01	88.12
RC + RH Flip	83.12	81.64	87.76	86.10	84.53	91.72	83.71	82.37	86.69
RC + RV + RH Flip	84.59	85.25	88.84	83.38	81.65	86.33	82.57	82.73	85.97
RR	87.80	87.76	91.72	89.27	88.13	94.96	88.72	89.92	92.80

Image Accuracy (IA), First Image Accuracy (FIA) and Leaf Accuracy (LA) are compared to baseline accuracies obtained without data augmentation. The highest accuracy in each column is highlighted in bold. All models here employ the Adam optimizer with learning rate (lr) \(=1e^{-4}\)
RV Random Vertical, RH Random Horizontal, RC Random Crop, RR Random Rotation

ISSN: 1746-4811