基于GAN-DCNN的树叶识别

doi:10.11707/j.1001-7488.LYKX20220847

Abstract

Abstract:

Objective: A large number of training samples are needed for leaf identification based on deep learning. Insufficient sample size and single image style can affect the identification accuracy. However, studying the use of a small number of samples for leaf image reproduction and style transformation can greatly reduce the burden of data collection, which would provide effective technical means and theoretical support for improving forestry survey informationize and intelligence. Method: The leaf images of 6 tree species were collected to establish a dataset, and the light-weight generating adversarial networks (GAN) was introduced to propagate images and style translation, and expand the manually shot leaf dataset. Four deep convolutional neural networks (DCNN), AlexNet, GoogLeNet, ResNet34 and ShuffleNetV2, were applied to train the dataset and the original dataset, respectively, by which the role of image augmentation techniques of GAN in leaf recognition was analyzed. The optimal model was selected based on performance indicators such as model accuracy and training time, and the learning rate was adjusted. Finally, the test samples were used to verify the optimized model, and the feasibility and significance of the method in practice were analyzed. Result: The samples with high definition and high fidelity were generated based on generative adversarial networks, which was able to effectively enrich the sample category, and obtain leaf images with different shapes, and health conditions at different seasons. Compared with the original dataset, AlexNet, GoogLeNet, ResNet34 and ShuffleNetV2 all showed smaller training errors and higher validating accuracy on new dataset during the model training. Among them, the ShuffleNetV2 model with a learning rate of 0.01 had the best training effect on this dataset, whose highest validating accuracy was 99.7%. The model was verified by using the test samples and a good recognition performance on each leaf was achieved, and the overall recognition accuracy of the model was up to 99.8%. Compared with the ordinary DCNN without GAN, the model proposed in this paper significantly improved the accuracy of leaf identification. Conclusion: GAN can effectively expand the number of images and transform the style of images. The combined use of GAN and DCNN can significantly improve the accuracy of leaf identification, thus it can be applied in forest leaf identification.

Key words: leaf identification, generative adversarial networks, deep convolutional neural networks

CLC Number:

TP183

Jingyi Xu,Zhi Zhang,Fei Yan,Wenyue Zhang. Leaf Identification Based on GAN-DCNN[J]. Scientia Silvae Sinicae, 2024, 60(4): 40-51.

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层名称 Layer name	输出尺寸 Output size	卷积核大小 Kernel size	步长 Stride	重复次数/次 Repeat times
输入图像 Image	224×224×3
卷积层1 Conv1	112×112×24	3×3	2	1
最大池化层MaxPool	56×56×24	3×3	2	1
残差单元堆叠层2 Stage2	28×28×116		2	1
残差单元堆叠层2 Stage2	28×28×116		1	3
残差单元堆叠层3 Stage3	14×14×232		2	1
残差单元堆叠层3 Stage3	14×14×232		1	7
残差单元堆叠层4 Stage4	7×7×464		2	1
残差单元堆叠层4 Stage4	7×7×464		1	3
卷积层5 Conv5	7×7×1024	1×1	1	1
全局池化层GlobalPool	1×1	7×7
全连接层 FC	channels=6
归一化指数函数 Softmax

树种 Tree species	原始图像 Original images	生成图像 Generated images
菜豆树 Radermachera sinica
鹅掌楸 Liriodendron chinense
灰莉 Fagraea ceilanica
肉桂 Cinnamomum cassia
五角槭 Acer pictum subsp. mono
鸭脚木 Heptapleurum heptaphyllum

	AlexNet	GoogLeNet	ResNet34	ShufffeNetV2
训练时长 Training time	32 min 4 s	93 min 59 s	133 min 37 s	43 min 27 s
训练最小损失 Minimum training loss	0.119	0.084	0.046	0.055
验证最高精度 Maximum validating accuracy	99%	99%	99.3%	99.4%
收敛速度 Rate of convergence	较快 Fast	快 Faster	较快 Fast	快 Faster
收敛后稳定性 Convergence stability	波动 Fluctuant	小波动 Small fluctuant	稳定 Stable	非常稳定 Very stable

学习率 Learning rate	0.001	0.01	0.1
训练时长 Training time	43 min 27 s	45 min	49 min 8 s
训练最小损失 Minimum training loss	0.055	0.02	0.02
验证最高精度 Maximum validating accuracy	99.4%	99.7%	99.5%
收敛速度 Rate of convergence	较快 Fast	最快 Fastest	快 Faster
收敛后稳定性 Convergence stability	稳定 More stable	最稳定 Most stable	较稳定 Stable

树种 Tree species	五角槭 Acer pictum subsp. mono	肉桂 Cinnamomum cassia	灰莉 Fagraea ceilanica	鹅掌楸 Liriodendron chinense	菜豆树 Radermachera sinica	鸭脚木 Heptapleurum heptaphyllum	总和Total
五角槭 Acer pictum subsp. mono	111	0	0	0	0	0	111
肉桂 Cinnamomum cassia	0	169	0	0	0	0	169
灰莉 Fagraea ceilanica	0	0	167	0	0	0	167
鹅掌楸 Liriodendron chinense	1	0	0	139	0	0	140
菜豆树 Radermachera sinica	0	0	0	0	152	0	152
鸭脚木 Heptapleurum heptaphyllum	0	0	1	0	0	137	138
总和Total	112	169	168	139	152	137	877
准确率Accuracy rate	99.1%	100%	99.4%	100%	100%	100%	100%
总体准确率 Total accuracy rate	99.8%

Leaf Identification Based on GAN-DCNN

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树种 Tree species	分类模型 Classification model	幼龄叶分类准确率 Classification accuracy of young leaves（%）	成熟叶分类准确率 Classification accuracy of mature leaves（%）	衰老叶分类准确率 Classification accuracy of senescent leaves（%）
五角槭 Acer pictum subsp. mono	GAN-DCNN	100	100	100
五角槭 Acer pictum subsp. mono	DCNN	100	100	100
肉桂Cinnamomum cassia	GAN-DCNN	100	100	100
肉桂Cinnamomum cassia	DCNN	100	100	100
灰莉 Fagraea ceilanica	GAN-DCNN	100	100	100
灰莉 Fagraea ceilanica	DCNN	100	100	100
鹅掌楸Liriodendron chinense	GAN-DCNN	100	100	100
鹅掌楸Liriodendron chinense	DCNN	100	100	100
菜豆树Radermachera sinica	GAN-DCNN	100	100	100
菜豆树Radermachera sinica	DCNN	100	100	100
鸭脚木Heptapleurum heptaphyllum	GAN-DCNN	100	100	100
鸭脚木Heptapleurum heptaphyllum	DCNN	100	80	60

树种 Tree species	分类模型 Classification model	幼龄叶 Young leaves	识别结果 Classification results（%）	成熟叶 Mature leaves	识别结果 Classification results（%）	衰老叶 Senescent leaves	识别结果 Classification results（%）
五角槭 Acer pictum subsp. mono	GAN-DCNN		100		100		100
五角槭 Acer pictum subsp. mono	DCNN		97.3		84.9		87.6
肉桂Cinnamomum cassia	GAN-DCNN		100		99.6		100
肉桂Cinnamomum cassia	DCNN		99.7		55.3		98.4
灰莉 Fagraea ceilanica	GAN-DCNN		99.9		100		100
灰莉 Fagraea ceilanica	DCNN		59.3		70.3		96
鹅掌楸Liriodendron chinense	GAN-DCNN		100		99.9		100
鹅掌楸Liriodendron chinense	DCNN		91.9		68.4		99.8
菜豆树Radermachera sinica	GAN-DCNN		100		100		100
菜豆树Radermachera sinica	DCNN		99.9		99.9		99.5
鸭脚木Heptapleurum heptaphyllum	GAN-DCNN		98.9		99.9		95
鸭脚木Heptapleurum heptaphyllum	DCNN		78.8		83.3		87.7

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