Application of UAV-Based Imaging and Deep Learning in Assessment of Rice Blast Resistance

doi:10.1016/j.rsci.2023.06.005

摘要/Abstract

. [J]. Rice Science, 2023, 30(6): 652-660.

图/表 7

参考文献 30

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Backbone	Training time (h)	Box loss	Mask loss	Classification loss	Segmentation loss
ResNet50	60.88	0.024	0.024	0.069	0.022
ResNet101	36.22	0.026	0.026	0.083	0.022
Swin-Tiny	30.23	0.097	0.065	0.040	0.044
Swin-Base	38.27	0.067	0.044	0.027	0.034

Backbone	Training time (h)	Box loss	Mask loss	Classification loss	Segmentation loss
ResNet50	60.88	0.024	0.024	0.069	0.022
ResNet101	36.22	0.026	0.026	0.083	0.022
Swin-Tiny	30.23	0.097	0.065	0.040	0.044
Swin-Base	38.27	0.067	0.044	0.027	0.034

Disease level	ResNet50	ResNet101	Swin-Tiny	Swin-Base	ResNet50	ResNet101	Swin-Tiny	Swin-Base
Box average precision					Mask average precision
1	100.00^a	100.00^a	80.00	100.00^a	90.00	97.33^a	60.00	80.00
2	98.88 ^a	98.86	98.01	98.77	95.95^a	95.42	90.52	93.07
3	97.88	98.16	98.21^a	97.97	94.65	95.46^a	91.13	93.96
4	98.99	99.00^a	98.29	98.98	97.27^a	96.73	91.45	93.98
5	100.00^a	100.00^a	100.00^a	100.00^a	100.00^a	95.05	95.05	95.05
6	98.99	99.00^a	98.42	99.00^a	97.04^a	96.80	91.47	93.91
7	98.80^a	98.77	97.90	98.74	96.26^a	95.59	90.17	92.95
8	97.97^a	97.85	97.29	97.86	93.99^a	93.71	89.31	91.08
9	98.87	98.89	98.25	98.98^a	97.55	97.33	92.76	97.56^a

Disease level	ResNet50	ResNet101	Swin-Tiny	Swin-Base	ResNet50	ResNet101	Swin-Tiny	Swin-Base
Box average precision					Mask average precision
1	100.00^a	100.00^a	80.00	100.00^a	90.00	97.33^a	60.00	80.00
2	98.88 ^a	98.86	98.01	98.77	95.95^a	95.42	90.52	93.07
3	97.88	98.16	98.21^a	97.97	94.65	95.46^a	91.13	93.96
4	98.99	99.00^a	98.29	98.98	97.27^a	96.73	91.45	93.98
5	100.00^a	100.00^a	100.00^a	100.00^a	100.00^a	95.05	95.05	95.05
6	98.99	99.00^a	98.42	99.00^a	97.04^a	96.80	91.47	93.91
7	98.80^a	98.77	97.90	98.74	96.26^a	95.59	90.17	92.95
8	97.97^a	97.85	97.29	97.86	93.99^a	93.71	89.31	91.08
9	98.87	98.89	98.25	98.98^a	97.55	97.33	92.76	97.56^a

Model	AP (IOU 0.50)	AP (IOU 0.75)	AP (IOU 0.90)	mAP (IOU 0.50‒0.95)
YOLACT	92.09	90.50	48.78	77.12
YOLACT++	93.10	90.53	49.86	77.83
Fast R-CNN	87.02	82.03	33.56	67.54
Mask R-CNN	95.03	87.14	57.35	79.84
Transfiner	95.08	85.33	58.33	79.58
RiceblastSegMask	96.83	87.33	56.33	80.16

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