Acta Agriculturae Zhejiangensis ›› 2021, Vol. 33 ›› Issue (7): 1329-1338.DOI: 10.3969/j.issn.1004-1524.2021.07.19
• Biosystems Engineering • Previous Articles Next Articles
ZHANG Ning1,2(), WU Huarui2,3,4,*(
), HAN Xiao2,3,4, MIAO Yisheng2,3,4
Received:
2020-09-25
Online:
2021-07-25
Published:
2021-08-06
Contact:
WU Huarui
网络层数 Number of network layers | 类型 Type | 滤波器 Filter shape | 输入尺寸 Input size |
---|---|---|---|
1 | 卷积层1 Convolutional layer 1 | 3×3 | 299×299×3 |
2 | 卷积层2 Convolutional layer 2 | 3×3 | 149×149×32 |
3 | 卷积层3 Convolutional layer 3 | 3×3 | 147×147×32 |
4 | 池化层1 Pooling layer 1 | 3×3 | 147×147×64 |
5 | 卷积层4 Convolutional layer 4 | 3×3 | 73×73×64 |
6 | 卷积层5 Convolutional layer 5 | 3×3 | 73×73×80 |
7 | 卷积层6 Convolutional layer 6 | 3×3 | 35×35×192 |
8 | Inception1模块组 Inception1 module group | 3个Inception1 3 Inception1 | 35×35×288 |
9 | Inception2模块组 Inception2 module group | 5个Inception2 5 Inception2 | 17×17×768 |
10 | Inception3模块组 Inception3 module group | 3个Inception3 3 Inception3 | 8×8×1 280 |
11 | CBAM模块 CBAM module | 7×7 | 8×8×2 048 |
12 | 全连接层 Fully connected layer | 分类器 Classifier | 1×2 048 |
Table 1 AT-InceptionV3 network architecture
网络层数 Number of network layers | 类型 Type | 滤波器 Filter shape | 输入尺寸 Input size |
---|---|---|---|
1 | 卷积层1 Convolutional layer 1 | 3×3 | 299×299×3 |
2 | 卷积层2 Convolutional layer 2 | 3×3 | 149×149×32 |
3 | 卷积层3 Convolutional layer 3 | 3×3 | 147×147×32 |
4 | 池化层1 Pooling layer 1 | 3×3 | 147×147×64 |
5 | 卷积层4 Convolutional layer 4 | 3×3 | 73×73×64 |
6 | 卷积层5 Convolutional layer 5 | 3×3 | 73×73×80 |
7 | 卷积层6 Convolutional layer 6 | 3×3 | 35×35×192 |
8 | Inception1模块组 Inception1 module group | 3个Inception1 3 Inception1 | 35×35×288 |
9 | Inception2模块组 Inception2 module group | 5个Inception2 5 Inception2 | 17×17×768 |
10 | Inception3模块组 Inception3 module group | 3个Inception3 3 Inception3 | 8×8×1 280 |
11 | CBAM模块 CBAM module | 7×7 | 8×8×2 048 |
12 | 全连接层 Fully connected layer | 分类器 Classifier | 1×2 048 |
Fig.4 Confusion matrix of recognition results 0, Tomato bacterial spot; 1, Tomato healthy leaf; 2, Tomato late blight; 3, Tomato leaf mold; 4, Tomato yellow leaf curl virus.
病害类型 Types of disease | InceptionV3从零训练 InceptionV3 training from zero | InceptionV3迁移学习 InceptionV3 transfer learning | AT-InceptionV3迁移学习 AT-InceptionV3 transfer learning | |||
---|---|---|---|---|---|---|
准确率 Accuracy | 召回率 Recall | 准确率 Accuracy | 召回率 Recall | 准确率 Accuracy | 召回率 Recall | |
细菌性斑疹病Bacterial spot | 58.88 | 56.60 | 96.30 | 93.90 | 93.75 | 97.40 |
健康叶Healthy leaf | 87.50 | 74.50 | 100.0 | 97.60 | 100.0 | 96.38 |
晚疫病Late blight | 61.30 | 64.50 | 95.00 | 98.70 | 98.75 | 98.75 |
叶霉病Leaf mold | 62.50 | 67.60 | 96.30 | 96.30 | 98.75 | 98.75 |
黄曲病Yellow leaf curl virus | 65.00 | 71.20 | 97.50 | 98.70 | 98.75 | 98.75 |
Table 2 Recognition rate of different schemes in each type of disease image %
病害类型 Types of disease | InceptionV3从零训练 InceptionV3 training from zero | InceptionV3迁移学习 InceptionV3 transfer learning | AT-InceptionV3迁移学习 AT-InceptionV3 transfer learning | |||
---|---|---|---|---|---|---|
准确率 Accuracy | 召回率 Recall | 准确率 Accuracy | 召回率 Recall | 准确率 Accuracy | 召回率 Recall | |
细菌性斑疹病Bacterial spot | 58.88 | 56.60 | 96.30 | 93.90 | 93.75 | 97.40 |
健康叶Healthy leaf | 87.50 | 74.50 | 100.0 | 97.60 | 100.0 | 96.38 |
晚疫病Late blight | 61.30 | 64.50 | 95.00 | 98.70 | 98.75 | 98.75 |
叶霉病Leaf mold | 62.50 | 67.60 | 96.30 | 96.30 | 98.75 | 98.75 |
黄曲病Yellow leaf curl virus | 65.00 | 71.20 | 97.50 | 98.70 | 98.75 | 98.75 |
原标签 True lable | 晚期病害Late disease | 早期病害Early diease | |||||
---|---|---|---|---|---|---|---|
图片 Images | 预测标签与置信度 Predict label and confidence degree | 图片 Images | 预测标签与置信度 Predict label and confidence degree | ||||
番茄细菌 性斑疹病 Tomato bacterial spot | ![]() | 番茄细菌性斑疹病(score=0.972 39) Tomato bacterial spot (score=0.972 39) 番茄叶霉病(score=0.014 88) Tomato leaf mold (score=0.014 88) 番茄黄曲病(score=0.007 67) Tomato yellow leaf curl virus (score=0.007 67) 番茄晚疫病(score=0.003 56) Tomato late blight (score=0.003 56) 番茄健康叶(score=0.001 49) Tomato healthy leaf(score=0.001 49) | ![]() | 番茄细菌性斑疹病(score=0.941 05) Tomato bacterial spot (score=0.941 05) 番茄健康叶(score=0.027 93) Tomato healthy leaf(score=0.027 93) 番茄叶霉病(score=0.014 04) Tomato leaf mold (score=0.014 04) 番茄黄曲病(score=0.012 65) Tomato yellow leaf curl virus (score=0.012 65) 番茄晚疫病(score=0.004 32) Tomato late blight (score=0.004 32) | |||
番茄健 康叶 Tomato healthy leaf | ![]() | 番茄健康叶(score=0.832 44) Tomato healthy leaf(score=0.832 44) 番茄晚疫病(score=0.081 10) Tomato late blight (score=0.081 10) 番茄叶霉病(score=0.080 07) Tomato leaf mold (score=0.080 07) 番茄黄曲病(score=0.004 26) Tomato yellow leaf curl virus (score=0.004 26) 番茄细菌性斑疹病(score=0.002 13) Tomato bacterial spot (score=0.002 13) | ![]() | 番茄健康叶(score=0.918 93) Tomato healthy leaf(score=0.918 93) 番茄晚疫病(score=0.070 37) Tomato late blight (score=0.070 37) 番茄细菌性斑疹病(score=0.006 04) Tomato bacterial spot (score=0.006 04) 番茄黄曲病(score=0.003 07) Tomato yellow leaf curl virus (score=0.003 07) 番茄叶霉病(score=0.001 60) Tomato leaf mold (score=0.001 60) | |||
番茄晚 疫病 Tomato late blight | ![]() | 番茄晚疫病(score=0.999 18) Tomato late blight (score=0.999 18) 番茄细菌性斑疹病(score=0.000 56) Tomato bacterial spot (score=0.000 56) 番茄叶霉病(score=0.000 11) Tomato leaf mold (score=0.000 11) 番茄健康叶(score=0.000 10) Tomato healthy leaf(score=0.000 10) 番茄黄曲病(score=0.000 05) Tomato yellow leaf curl virus (score=0.000 05) | ![]() | 番茄晚疫病(score=0.621 03) Tomato late blight (score=0.621 03) 番茄细菌性斑疹病(score=0.139 23) Tomato bacterial spot (score=0.139 23) 番茄健康叶(score=0.130 01) Tomato healthy leaf(score=0.130 01) 番茄黄曲病(score=0.089 71) Tomato yellow leaf curl virus (score=0.089 71) 番茄叶霉病(score=0.020 02) Tomato leaf mold (score=0.020 02) | |||
番茄叶 霉病 Tomato leaf mold | ![]() | 番茄叶霉病(score=0.994 35) Tomato leaf mold (score=0.994 35) 番茄晚疫病(score=0.002 57) Tomato late blight (score=0.002 57) 番茄黄曲病(score=0.001 33) Tomato yellow leaf curl virus (score=0.001 33) 番茄健康叶(score=0.001 05) Tomato healthy leaf (score=0.001 05) 番茄细菌性斑疹病(score=0.000 70) Tomato bacterial spot (score=0.000 70) | ![]() | 番茄叶霉病(score=0.992 87) Tomato leaf mold (score=0.992 87) 番茄细菌性斑疹病(score=0.004 54) Tomato bacterial spot (score=0.004 54) 番茄晚疫病(score=0.001 39) Tomato late blight (score=0.001 39) 番茄黄曲病(score=0.001 09) Tomato yellow leaf curl virus (score=0.001 09) 番茄健康叶(score=0.000 11) Tomato healthy leaf(score=0.000 11) | |||
原标签 True lable | 晚期病害Late disease | 早期病害Early diease | |||||
图片 Images | 预测标签与置信度 Predict label and confidence degree | 图片 Images | 预测标签与置信度 Predict label and confidence degree | ||||
番茄黄 曲病 Tomato yellow leaf curl virus | ![]() | 番茄黄曲病(score=0.993 05) Tomato yellow leaf curl virus (score=0.993 05) 番茄细菌性斑疹病(score=0.003 78) Tomato bacterial spot (score=0.003 78) 番茄晚疫病(score=0.001 20) Tomato late blight (score=0.001 20) 番茄叶霉病(score=0.001 20) Tomato leaf mold (score=0.001 20) 番茄健康叶(score=0.000 77) Tomato healthy leaf(score=0.000 77) | ![]() | 番茄黄曲病(score=0.975 95) Tomato yellow leaf curl virus (score=0.975 95) 番茄叶霉病(score=0.008 98) Tomato leaf mold (score=0.008 98) 番茄健康叶(score=0.008 70) Tomato healthy leaf(score=0.008 70) 番茄细菌性斑疹病(score=0.005 33) Tomato bacterial spot (score=0.005 33) 番茄晚疫病(score=0.001 04) Tomato late blight (score=0.001 04) |
Table 3 Predicted labels and confidence of tomato disease images on the AT-InceptionV3 model
原标签 True lable | 晚期病害Late disease | 早期病害Early diease | |||||
---|---|---|---|---|---|---|---|
图片 Images | 预测标签与置信度 Predict label and confidence degree | 图片 Images | 预测标签与置信度 Predict label and confidence degree | ||||
番茄细菌 性斑疹病 Tomato bacterial spot | ![]() | 番茄细菌性斑疹病(score=0.972 39) Tomato bacterial spot (score=0.972 39) 番茄叶霉病(score=0.014 88) Tomato leaf mold (score=0.014 88) 番茄黄曲病(score=0.007 67) Tomato yellow leaf curl virus (score=0.007 67) 番茄晚疫病(score=0.003 56) Tomato late blight (score=0.003 56) 番茄健康叶(score=0.001 49) Tomato healthy leaf(score=0.001 49) | ![]() | 番茄细菌性斑疹病(score=0.941 05) Tomato bacterial spot (score=0.941 05) 番茄健康叶(score=0.027 93) Tomato healthy leaf(score=0.027 93) 番茄叶霉病(score=0.014 04) Tomato leaf mold (score=0.014 04) 番茄黄曲病(score=0.012 65) Tomato yellow leaf curl virus (score=0.012 65) 番茄晚疫病(score=0.004 32) Tomato late blight (score=0.004 32) | |||
番茄健 康叶 Tomato healthy leaf | ![]() | 番茄健康叶(score=0.832 44) Tomato healthy leaf(score=0.832 44) 番茄晚疫病(score=0.081 10) Tomato late blight (score=0.081 10) 番茄叶霉病(score=0.080 07) Tomato leaf mold (score=0.080 07) 番茄黄曲病(score=0.004 26) Tomato yellow leaf curl virus (score=0.004 26) 番茄细菌性斑疹病(score=0.002 13) Tomato bacterial spot (score=0.002 13) | ![]() | 番茄健康叶(score=0.918 93) Tomato healthy leaf(score=0.918 93) 番茄晚疫病(score=0.070 37) Tomato late blight (score=0.070 37) 番茄细菌性斑疹病(score=0.006 04) Tomato bacterial spot (score=0.006 04) 番茄黄曲病(score=0.003 07) Tomato yellow leaf curl virus (score=0.003 07) 番茄叶霉病(score=0.001 60) Tomato leaf mold (score=0.001 60) | |||
番茄晚 疫病 Tomato late blight | ![]() | 番茄晚疫病(score=0.999 18) Tomato late blight (score=0.999 18) 番茄细菌性斑疹病(score=0.000 56) Tomato bacterial spot (score=0.000 56) 番茄叶霉病(score=0.000 11) Tomato leaf mold (score=0.000 11) 番茄健康叶(score=0.000 10) Tomato healthy leaf(score=0.000 10) 番茄黄曲病(score=0.000 05) Tomato yellow leaf curl virus (score=0.000 05) | ![]() | 番茄晚疫病(score=0.621 03) Tomato late blight (score=0.621 03) 番茄细菌性斑疹病(score=0.139 23) Tomato bacterial spot (score=0.139 23) 番茄健康叶(score=0.130 01) Tomato healthy leaf(score=0.130 01) 番茄黄曲病(score=0.089 71) Tomato yellow leaf curl virus (score=0.089 71) 番茄叶霉病(score=0.020 02) Tomato leaf mold (score=0.020 02) | |||
番茄叶 霉病 Tomato leaf mold | ![]() | 番茄叶霉病(score=0.994 35) Tomato leaf mold (score=0.994 35) 番茄晚疫病(score=0.002 57) Tomato late blight (score=0.002 57) 番茄黄曲病(score=0.001 33) Tomato yellow leaf curl virus (score=0.001 33) 番茄健康叶(score=0.001 05) Tomato healthy leaf (score=0.001 05) 番茄细菌性斑疹病(score=0.000 70) Tomato bacterial spot (score=0.000 70) | ![]() | 番茄叶霉病(score=0.992 87) Tomato leaf mold (score=0.992 87) 番茄细菌性斑疹病(score=0.004 54) Tomato bacterial spot (score=0.004 54) 番茄晚疫病(score=0.001 39) Tomato late blight (score=0.001 39) 番茄黄曲病(score=0.001 09) Tomato yellow leaf curl virus (score=0.001 09) 番茄健康叶(score=0.000 11) Tomato healthy leaf(score=0.000 11) | |||
原标签 True lable | 晚期病害Late disease | 早期病害Early diease | |||||
图片 Images | 预测标签与置信度 Predict label and confidence degree | 图片 Images | 预测标签与置信度 Predict label and confidence degree | ||||
番茄黄 曲病 Tomato yellow leaf curl virus | ![]() | 番茄黄曲病(score=0.993 05) Tomato yellow leaf curl virus (score=0.993 05) 番茄细菌性斑疹病(score=0.003 78) Tomato bacterial spot (score=0.003 78) 番茄晚疫病(score=0.001 20) Tomato late blight (score=0.001 20) 番茄叶霉病(score=0.001 20) Tomato leaf mold (score=0.001 20) 番茄健康叶(score=0.000 77) Tomato healthy leaf(score=0.000 77) | ![]() | 番茄黄曲病(score=0.975 95) Tomato yellow leaf curl virus (score=0.975 95) 番茄叶霉病(score=0.008 98) Tomato leaf mold (score=0.008 98) 番茄健康叶(score=0.008 70) Tomato healthy leaf(score=0.008 70) 番茄细菌性斑疹病(score=0.005 33) Tomato bacterial spot (score=0.005 33) 番茄晚疫病(score=0.001 04) Tomato late blight (score=0.001 04) |
模型 Model | 迁移学习 Transfer learning | CBAM模块 CBAM module | 准确率 Accuracy/% | 运行时间 Training time/s |
---|---|---|---|---|
InceptionV3从零训练 InceptionV3 training from zero | 67.8 | 93 000 | ||
InceptionV3迁移学习 InceptionV3 transfer learning | √ | 97.7 | 1 966 | |
AT-InceptionV3迁移学习 AT-InceptionV3 transfer learning | √ | √ | 98.4 | 2 031 |
Table 4 Experimental setup and comparison of results
模型 Model | 迁移学习 Transfer learning | CBAM模块 CBAM module | 准确率 Accuracy/% | 运行时间 Training time/s |
---|---|---|---|---|
InceptionV3从零训练 InceptionV3 training from zero | 67.8 | 93 000 | ||
InceptionV3迁移学习 InceptionV3 transfer learning | √ | 97.7 | 1 966 | |
AT-InceptionV3迁移学习 AT-InceptionV3 transfer learning | √ | √ | 98.4 | 2 031 |
图像分辨率 Images resolution | 单个病害识别准确率Single disease recognition accuracy | 平均准确率 Average accuracy | ||||
---|---|---|---|---|---|---|
细菌性斑疹病 Bacterial spot | 健康叶 Healthy leaf | 晚疫病 Late blight | 叶霉病 Leaf mold | 黄曲病 Yellow leaf curl virus | ||
256×256 | 93.75 | 100.00 | 98.75 | 98.75 | 98.75 | 98.00 |
240×240 | 99.00 | 97.00 | 99.00 | 97.00 | 100.00 | 98.40 |
224×224 | 99.00 | 97.00 | 98.00 | 98.00 | 100.00 | 98.40 |
128×128 | 99.00 | 100.00 | 98.00 | 99.00 | 100.00 | 99.20 |
Table 5 Experimental results of different resolution images %
图像分辨率 Images resolution | 单个病害识别准确率Single disease recognition accuracy | 平均准确率 Average accuracy | ||||
---|---|---|---|---|---|---|
细菌性斑疹病 Bacterial spot | 健康叶 Healthy leaf | 晚疫病 Late blight | 叶霉病 Leaf mold | 黄曲病 Yellow leaf curl virus | ||
256×256 | 93.75 | 100.00 | 98.75 | 98.75 | 98.75 | 98.00 |
240×240 | 99.00 | 97.00 | 99.00 | 97.00 | 100.00 | 98.40 |
224×224 | 99.00 | 97.00 | 98.00 | 98.00 | 100.00 | 98.40 |
128×128 | 99.00 | 100.00 | 98.00 | 99.00 | 100.00 | 99.20 |
原标签 True lable | 分辨率 Resolution | 图片 Images | 预测标签与置信度 Predict label and confidence degree | 图片 Images | 预测标签与置信度 Predict label and confidence degree |
---|---|---|---|---|---|
番茄细菌 性斑疹病 Tomato bacterial spot | 256×256 | ![]() | 番茄黄曲病(score=0.492 34) Tomato yellow leaf curl virus (score=0.492 34) 番茄细菌性斑疹病(score=0.438 02) Tomato bacterial spot (score=0.438 02) 番茄健康叶(score=0.030 68) Tomato healthy leaf(score=0.030 68) 番茄叶霉病(score=0.030 27) Tomato leaf mold (score=0.030 27) 番茄晚疫病(score=0.008 68) Tomato late blight (score=0.008 68) | ![]() | 番茄晚疫病(score=0.513 22) Tomato late blight (score=0.513 22) 番茄细菌性斑疹病(score=0.393 29) Tomato bacterial spot (score=0.393 29) 番茄叶霉病(score=0.086 98) Tomato leaf mold (score=0.086 98) 番茄黄曲病(score=0.005 80) Tomato yellow leaf curl virus (score=0.005 80) 番茄健康叶(score=0.000 70) Tomato healthy leaf(score=0.000 70) |
番茄细菌性斑疹病 Tomato bacterial spot | 240×240 | ![]() | 番茄黄曲病(score=0.649 63) Tomato yellow leaf curl virus(score=0.649 63) 番茄细菌性斑疹病(score=0.288 60) Tomato bacterial spot (score=0.288 60) 番茄健康叶(score=0.032 27) Tomato healthy leaf(score=0.032 27) 番茄叶霉病(score=0.017 99) Tomato leaf mold (score=0.017 99) 番茄晚疫病(score=0.011 51) Tomato late blight (score=0.011 51) | ![]() | 番茄细菌性斑疹病(score=0.684 45) Tomato bacterial spot (score=0.684 45) 番茄晚疫病(score=0.242 76) Tomato late bligh (score=0.242 76) 番茄叶霉病(score=0.067 28) Tomato leaf mold (score=0.067 28) 番茄黄曲病(score=0.004 97) Tomato yellow leaf curl virus (score=0.004 97) 番茄健康叶(score=0.000 53) Tomato healthy leaf (score=0.000 53) |
番茄细菌性斑疹病 Tomato bacterial spot | 224×224 | ![]() | 番茄黄曲病(score=0.640 14) Tomato yellow leaf curl virus (score=0.640 14) 番茄细菌性斑疹病(score=0.309 77) Tomato bacterial spot (score=0.309 77) 番茄健康叶(score=0.023 06) Tomato healthy leaf(score=0.023 06) 番茄叶霉病(score=0.017 49) Tomato leaf mold (score=0.017 49) 番茄晚疫病(score=0.009 55) Tomato late blight (score=0.009 55) | ![]() | 番茄细菌性斑疹病(score=0.768 61) Tomato bacterial spot (score=0.768 61) 番茄晚疫病(score=0.160 56) Tomato late blight (score=0.160 56) 番茄叶霉病(score=0.064 90) Tomato leaf mold (score=0.064 90) 番茄黄曲病(score=0.005 26) Tomato yellow leaf curl virus (score=0.005 26) 番茄健康叶(score=0.000 66) Tomato healthy leaf (score=0.000 66) |
番茄细菌性斑疹病 Tomato Bacterial spot | 128×128 | ![]() | 番茄细菌性斑疹病(score=0.491 46) Tomato bacterial spot (score=0.491 46) 番茄黄曲病(score=0.475 77) Tomato yellow leaf curl virus (score=0.475 77) 番茄叶霉病(score=0.017 26) Tomato leaf mold (score=0.017 26) 番茄晚疫病(score=0.010 84) Tomato late blight (score=0.010 84) 番茄健康叶(score=0.004 68) Tomato healthy leaf (score=0.004 68) | ![]() | 番茄细菌性斑疹病(score=0.623 09) Tomato bacterial spot (score=0.623 09) 番茄晚疫病(score=0.270 19) Tomato late bligh (score=0.270 19) 番茄叶霉病(score=0.093 92) Tomato leaf mold (score=0.093 92) 番茄黄曲病(score=0.012 38) Tomato yellow leaf curl virus (score=0.012 38) 番茄健康叶(score=0.000 43) Tomato healthy leaf (score=0.000 43) |
Table 6 Predictive label and confidence level of tomato bacterial spot disease images with different resolutions on AT-InceptionV3 model
原标签 True lable | 分辨率 Resolution | 图片 Images | 预测标签与置信度 Predict label and confidence degree | 图片 Images | 预测标签与置信度 Predict label and confidence degree |
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番茄细菌 性斑疹病 Tomato bacterial spot | 256×256 | ![]() | 番茄黄曲病(score=0.492 34) Tomato yellow leaf curl virus (score=0.492 34) 番茄细菌性斑疹病(score=0.438 02) Tomato bacterial spot (score=0.438 02) 番茄健康叶(score=0.030 68) Tomato healthy leaf(score=0.030 68) 番茄叶霉病(score=0.030 27) Tomato leaf mold (score=0.030 27) 番茄晚疫病(score=0.008 68) Tomato late blight (score=0.008 68) | ![]() | 番茄晚疫病(score=0.513 22) Tomato late blight (score=0.513 22) 番茄细菌性斑疹病(score=0.393 29) Tomato bacterial spot (score=0.393 29) 番茄叶霉病(score=0.086 98) Tomato leaf mold (score=0.086 98) 番茄黄曲病(score=0.005 80) Tomato yellow leaf curl virus (score=0.005 80) 番茄健康叶(score=0.000 70) Tomato healthy leaf(score=0.000 70) |
番茄细菌性斑疹病 Tomato bacterial spot | 240×240 | ![]() | 番茄黄曲病(score=0.649 63) Tomato yellow leaf curl virus(score=0.649 63) 番茄细菌性斑疹病(score=0.288 60) Tomato bacterial spot (score=0.288 60) 番茄健康叶(score=0.032 27) Tomato healthy leaf(score=0.032 27) 番茄叶霉病(score=0.017 99) Tomato leaf mold (score=0.017 99) 番茄晚疫病(score=0.011 51) Tomato late blight (score=0.011 51) | ![]() | 番茄细菌性斑疹病(score=0.684 45) Tomato bacterial spot (score=0.684 45) 番茄晚疫病(score=0.242 76) Tomato late bligh (score=0.242 76) 番茄叶霉病(score=0.067 28) Tomato leaf mold (score=0.067 28) 番茄黄曲病(score=0.004 97) Tomato yellow leaf curl virus (score=0.004 97) 番茄健康叶(score=0.000 53) Tomato healthy leaf (score=0.000 53) |
番茄细菌性斑疹病 Tomato bacterial spot | 224×224 | ![]() | 番茄黄曲病(score=0.640 14) Tomato yellow leaf curl virus (score=0.640 14) 番茄细菌性斑疹病(score=0.309 77) Tomato bacterial spot (score=0.309 77) 番茄健康叶(score=0.023 06) Tomato healthy leaf(score=0.023 06) 番茄叶霉病(score=0.017 49) Tomato leaf mold (score=0.017 49) 番茄晚疫病(score=0.009 55) Tomato late blight (score=0.009 55) | ![]() | 番茄细菌性斑疹病(score=0.768 61) Tomato bacterial spot (score=0.768 61) 番茄晚疫病(score=0.160 56) Tomato late blight (score=0.160 56) 番茄叶霉病(score=0.064 90) Tomato leaf mold (score=0.064 90) 番茄黄曲病(score=0.005 26) Tomato yellow leaf curl virus (score=0.005 26) 番茄健康叶(score=0.000 66) Tomato healthy leaf (score=0.000 66) |
番茄细菌性斑疹病 Tomato Bacterial spot | 128×128 | ![]() | 番茄细菌性斑疹病(score=0.491 46) Tomato bacterial spot (score=0.491 46) 番茄黄曲病(score=0.475 77) Tomato yellow leaf curl virus (score=0.475 77) 番茄叶霉病(score=0.017 26) Tomato leaf mold (score=0.017 26) 番茄晚疫病(score=0.010 84) Tomato late blight (score=0.010 84) 番茄健康叶(score=0.004 68) Tomato healthy leaf (score=0.004 68) | ![]() | 番茄细菌性斑疹病(score=0.623 09) Tomato bacterial spot (score=0.623 09) 番茄晚疫病(score=0.270 19) Tomato late bligh (score=0.270 19) 番茄叶霉病(score=0.093 92) Tomato leaf mold (score=0.093 92) 番茄黄曲病(score=0.012 38) Tomato yellow leaf curl virus (score=0.012 38) 番茄健康叶(score=0.000 43) Tomato healthy leaf (score=0.000 43) |
[1] | 刘雪霞. 智慧农业与无线传感器应用分析[J]. 电子技术与软件工程, 2020(11):114-115. |
LIU X X. Smart agriculture and wireless sensor application analysis[J]. Electronic Technology & Software Engineering, 2020(11):114-115.(in Chinese) | |
[2] | 张连宽, 肖德琴, 李就好. 基于无线传感器的作物图像传输方法[J]. 广东农业科学, 2013, 40(15):176-179. |
ZHANG L K, XIAO D Q, LI J H. Crop image transmission in wireless image sensor networks[J]. Guangdong Agricultural Sciences, 2013, 40(15):176-179.(in Chinese with English abstract) | |
[3] | 李凯雨. 基于深度学习的农作物病害识别[D]. 郑州: 河南农业大学, 2018. |
LI K Y. The identification of crop diseases based on deep learning[D]. Zhengzhou: Henan Agricultural University, 2018. (in Chinese with English abstract) | |
[4] | SRIVASTAVA A, MA S, INOUE K. Development of a sensor for automatic detection of downey mildew disease [C]//International Conference on Intelligent Mechatronics and Automation, 2004:562-567. |
[5] | 王守志, 何东健, 李文, 等. 基于核K-均值聚类算法的植物叶部病害识别[J]. 农业机械学报, 2009, 40(3):152-155. |
WANG S Z, HE D J, LI W, et al. Plant leaf disease recognition based on kernel K-means clustering algorithm[J]. Transactions of the Chinese Society for Agricultural Machinery, 2009, 40(3):152-155.(in Chinese with English abstract) | |
[6] | 殷建军, 潘春华, 肖克辉, 等. 基于无线图像传感器网络的农田远程监测系统[J]. 农业机械学报, 2017, 48(7):286-293. |
YIN J J, PAN C H, XIAO K H, et al. Remote monitoring system for farmland based on wireless image sensor network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(7):286-293.(in Chinese with English abstract) | |
[7] | 申文韬. 5G通信技术与人工智能技术融合发展的基本现状与演化趋势[J]. 计算机产品与流通, 2020(7):38. |
SHEN W T. The basic status and evolutionary trend of the integration and development of 5G communication technology and artificial intelligence technology[J]. Computer Products and Circulation, 2020(7):38. (in Chinese) | |
[8] | 郑远攀, 李广阳, 李晔. 深度学习在图像识别中的应用研究综述[J]. 计算机工程与应用, 2019, 55(12):20-36. |
ZHENG Y P, LI G Y, LI Y. Survey of application of deep learning in image recognition[J]. Computer Engineering and Applications, 2019, 55(12):20-36.(in Chinese with English abstract) | |
[9] | 张帅. 基于深度学习的植物叶片识别算法研究[D]. 北京: 北京林业大学, 2016. |
ZHANG S. Research on plant leaf images identification algorithm based on deep learning[D]. Beijing: Beijing Forestry University, 2016. (in Chinese with English abstract) | |
[10] | 黄双萍, 孙超, 齐龙, 等. 基于深度卷积神经网络的水稻穗瘟病检测方法[J]. 农业工程学报, 2017, 33(20):169-176. |
HUANG S P, SUN C, QI L, et al. Rice panicle blast identification method based on deep convolution neural network[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(20):169-176.(in Chinese with English abstract) | |
[11] | 郭小清, 范涛杰, 舒欣. 基于改进Multi-Scale AlexNet的番茄叶部病害图像识别[J]. 农业工程学报, 2019, 35(13):162-169. |
GUO X Q, FAN T J, SHU X. Tomato leaf diseases recognition based on improved Multi-Scale AlexNet[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(13):162-169.(in Chinese with English abstract) | |
[12] | 吴华瑞. 基于深度残差网络的番茄叶片病害识别方法[J]. 智慧农业, 2019, 1(4):42-49. |
WU H R. Method of tomato leaf diseases recognition method based on deep residual network[J]. Smart Agriculture, 2019, 1(4):42-49.(in Chinese with English abstract) | |
[13] | SHAN C H, ZHANG J B, WANG Y J, et al. Attention-based end-to-end speech recognition on voice search [C]//2018 IEEE international conference on acoustics, speech and signal processing, 2018. |
[14] | ADAM R K, ALEX B, INGMAR P. Hierarchical attentive recurrent tracking [C]//2017 IEEE conference on computer vision and pattern recognition, 2017. |
[15] | CHU X, YANG W, OUYANG W, et al. Multi-context attention for human pose estimation [C]//2017 IEEE conference on computer vision and pattern recognition, 2017. |
[16] | 张盼盼, 李其申, 杨词慧. 基于轻量级分组注意力模块的图像分类算法[J]. 计算机应用, 2020, 40(3):645-650. |
ZHANG P P, LI Q S, YANG C H. Image classification algorithm based on lightweight group-wise attention module[J]. Journal of Computer Applications, 2020, 40(3):645-650.(in Chinese with English abstract) | |
[17] | 袁建野, 南新元, 李成荣, 等. 注意力机制下的轻量级垃圾分类网络[J/OL]. 计算机工程, 2020. https://doi.org/10.19678/j.issn.1000-3428.0058108 . |
YUAN J Y, NAN X Y, LI C R, et al. Lightweight garbage classification network based on attention mechanism. Computer Engineering, 2020. https://doi.org/10.19678/j.issn.1000-3428.0058108 . (in Chinese with English abstract) | |
[18] | 胡志伟, 杨华, 黄济民, 等. 基于注意力残差机制的细粒度番茄病害识别[J]. 华南农业大学学报, 2019, 40(6):124-132. |
HU Z W, YANG H, HUANG J M, et al. Fine-grained tomato disease recognition based on attention residual mechanism[J]. Journal of South China Agricultural University, 2019, 40(6):124-132.(in Chinese with English abstract) | |
[19] | WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[M]//Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018: 3-19. |
[20] | DENG J, DONG W, SOCHER R, et al. ImageNet: a large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. June 20-25, 2009, Miami, FL, USA. IEEE, 2009: 248-255. |
[21] | 林朝剑, 张广群, 杨洁, 等. 基于迁移学习的林业业务图像识别[J]. 南京林业大学学报(自然科学版), 2020, 44(4):215-221. |
LIN C J, ZHANG G Q, YANG J, et al. Transfer learning based recognition for forestry business images[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2020, 44(4):215-221.(in Chinese with English abstract) |
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