Pear leaf disease segmentation method based on improved DeepLabv3+

Figures & data

Figure 1. Part of the pear leaf disease dataset.

Figure 2. Examples of data augmentation. (a) original image; (b) Gaussian blur; (c) horizontal flip; (d) rotation by 90°; (e) brightening.

Table 1. Dataset.

Sort	Number of pictures/(pictures)
Black spot	928
Brown spot	464
Roll gall stripe	352
Healthy leaves	260
Total	2004

Figure 3. Examples of annotated images.

Figure 4. DeepLabv3+ model.

Table 2. Mobilenetv2 structure parameter table.

Input	Operator	Coefficient of expansion	Output channel number	Sequence repetition	Step size
224 × 224 × 3	Conv2d	–	32	1	2
112 × 112 × 32	bottleneck	1	16	1	1
112 × 112 × 16	bottleneck	6	24	2	2
56 × 56 × 24	bottleneck	6	32	3	2
28 × 28 × 32	bottleneck	6	64	4	2
14 × 14 × 64	bottleneck	6	96	3	1
14 × 14 × 96	bottleneck	6	160	3	2
7 × 7 × 160	bottleneck	6	320	1	1
7 × 7 × 320	Conv2d1 × 1	–	1280	1	1
7 × 7 × 1280	avgpool7 × 7	–	–	1	–
1 × 1 × 1280	Conv2d1 × 1	–	k	–	–

Figure 5. SE attention mechanism.

Figure 6. Improved DeepLabv3+ model.

Table 3. Table of experimental environment parameters.

Experimental environment	Configuration
Operating system	Windows10
CPU	Intel(R) Xeon(R) Platinum 8255 C CPU @ 2.50 GHz
RAM	32GB
CUDA	11.7
Python	3.10
PyTorch	1.8.1

Table 4. Experimental parameter settings.

Experimental parameters	Configuration
Input image	512 × 512
Learning rate	5e-4
Epoch	300
batch_size	4
Optimizer	Adam

Figure 7. Comparison of loss rate before and after improvement.

Table 5. Comparison of influence of different backbone networks.

Backbone	MIoU(%)	mPA(%)	mPr(%)	mRecall(%)	mate(s)	Run time(h)
Xception	82.02%	86.91%	90.03%	86.96%	269.76	22.48
MobileNetV2	83.77%	88.77%	91.00%	88.77%	62.28	5.19

Table 6. Comparison of the effects of different optimizers.

Backbone	SGD	Adam	MIoU(%)	mPA(%)	mPr(%)	mRecall(%)
Xception		√	82.02%	86.91%	90.03%	86.96%
Xception	√		78.31%	82.84%	89.71%	82.84%
MobileNetV2		√	83.77%	88.77%	91.00%	88.77%
MobileNetV2	√		79.32%	83.56%	89.11%	83.56%

√ means: the marked module was used in the re-experiment and no means that the module was not added.

Table 7. Comparison and verification of different attention mechanisms.

Model Name	CA	SE	CBAM	ECA	MIoU	mPA	mPrecision	mRecall	mate(s)
MobileNetV2					83.77	88.77	91.00	88.77	62.28
MobileNetV2 + CA	√				82.32	87.91	90.84	87.91	67.44
MobileNetV2 + ECA				√	83.02	89.29	90.62	89.29	66.60
MobileNetV2 + CBAM			√		83.79	88.14	91.43	88.14	67.44
MobileNetV2 + SE		√			86.32	88.97	91.10	88.97	61.80

√ Means: the marked module was used in the re-experiment, and no means that the module was not added.

Figure 8. Comparison of Iou of five labels under different models.

Table 8. Comparison of segmentation effects of different network models.

Model	MIOU(%)	mPA(%)	ACC(%)
CENet	41.43%	70.96%	99.2%
SegNet	78.27%	82.22%	90.99%
FCN	81.14%	85.29%	98.60%
PSPNet	81.51%	86.33%	98.15%
Deeplabv3+	82.02%	86.91%	99.16%
Improved Deeplabv3+	86.32%	88.97%	99.90%

Figure 9. Comparison of segmentation results of different network models. (a) Original; (b) labeled; (c) SegNet; (d) FCN; (e) PSPNet; (f) DeepLabv3+; (g) improved.

Data availability statement

The experimental data used to support the findings of this study are available from the authors upon request.