Segmentation and counting of wheat spike grains based on deep learning and textural feature

Xu, Xin; Geng, Qing; Gao, Feng; Xiong, Du; Qiao, Hongbo; Ma, Xinming

doi:10.1186/s13007-023-01062-6

Plant Methods

Table 2 Performance comparisons and evaluation of different segmentation models

From: Segmentation and counting of wheat spike grains based on deep learning and textural feature

Model	Backbone	Optimizer	Learning rate	Weight decay	Recall	Precision	Mean intersection over union	Mean pixel accuracy
CABM-HRNet	hrnetv2_w32	Adam	0.0005	0	0.9116 ± 0.099	0.9204 ± 0.107	0.8521 ± 0.034	0.9116 ± 0.099
	hrnetv2_w18	Adam	0.0005	0	0.9061 ± 0.097	0.9174 ± 0.138	0.8500 ± 0.027	0.9061 ± 0.097
	hrnetv2_w32	SGD	0.004	0.0001	0.8953 ± 0.099	0.9122 ± 0.123	0.8360 ± 0.023	0.8953 ± 0.099
	hrnetv2_w18	SGD	0.004	0.0001	0.8939 ± 0.100	0.9053 ± 0.168	0.8302 ± 0.029	0.8939 ± 0.100
HRNet	hrnetv2_w32	Adam	0.0005	0	0.9100 ± 0.110	0.9189 ± 0.131	0.8510 ± 0.042	0.9100 ± 0.110
	hrnetv2_w18	Adam	0.0005	0	0.9097 ± 0.125	0.9176 ± 0.142	0.8505 ± 0.040	0.9097 ± 0.125
	hrnetv2_w32	SGD	0.004	0.0001	0.8910 ± 0.126	0.9140 ± 0.130	0.8341 ± 0.043	0.8910 ± 0.126
	hrnetv2_w18	SGD	0.004	0.0001	0.8896 ± 0.175	0.9064 ± 0.139	0.8278 ± 0.056	0.8896 ± 0.175
PSPNet	MobileNetv2	Adam	0.0005	0	0.8995 ± 0.149	0.8883 ± 0.124	0.8221 ± 0.052	0.8995 ± 0.149
	ResNet50	Adam	0.0005	0	0.9018 ± 0.230	0.8939 ± 0.119	0.8278 ± 0.052	0.9018 ± 0.230
	MobileNetv2	SGD	0.01	0.0001	0.8566 ± 0.498	0.8554 ± 0.177	0.7718 ± 0.239	0.8566 ± 0.498
	ResNet50	SGD	0.01	0.0001	0.8777 ± 0.315	0.8900 ± 0.148	0.8082 ± 0.122	0.8777 ± 0.315
DeeplabV3 +	MobileNetv2	Adam	0.0005	0	0.9101 ± 0.151	0.9118 ± 0.147	0.8468 ± 0.051	0.9101 ± 0.151
	Xception	Adam	0.0005	0	0.9060 ± 0.135	0.9100 ± 0.143	0.8425 ± 0.038	0.9060 ± 0.135
	MobileNetv2	SGD	0.007	0.0001	0.8994 ± 0.265	0.8826 ± 0.148	0.8178 ± 0.064	0.8994 ± 0.265
	Xception	SGD	0.007	0.0001	0.8945 ± 0.265	0.8843 ± 0.109	0.8158 ± 0.055	0.8945 ± 0.265
U-Net	ResNet50	Adam	0.0001	0	0.9055 ± 0.103	0.9172 ± 0.145	0.8473 ± 0.035	0.9055 ± 0.103
	VGG	Adam	0.0001	0	0.9045 ± 0.044	0.9198 ± 0.162	0.8484 ± 0.014	0.9045 ± 0.044
	ResNet50	SGD	0.01	0.0001	0.8892 ± 0.303	0.8944 ± 0.142	0.8192 ± 0.084	0.8892 ± 0.303
	VGG	SGD	0.01	0.0001	0.8948 ± 0.084	0.9057 ± 0.137	0.8312 ± 0.020	0.8948 ± 0.084

Different backbone, optimizers, and learning rates were used according to different segmentation models. The evaluation indicators were measured on the test set with ten-fold cross-validation (mean ± standard deviation). The best results of each network are shown in bold

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ISSN: 1746-4811

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