Journal of Agricultural Science and Technology ›› 2022, Vol. 24 ›› Issue (7): 86-96.DOI: 10.13304/j.nykjdb.2021.0507
• INTELLIGENT AGRICULTURE & AGRICULTURAL MACHINERY • Previous Articles Next Articles
Kui FANG(), Cheng LI(
), Xiao HE, Yineng CHEN
Received:
2021-06-21
Accepted:
2021-11-22
Online:
2022-07-15
Published:
2022-08-15
Contact:
Cheng LI
通讯作者:
李成
作者简介:
方逵E-mail:fk@hunau.edu.cn;
基金资助:
CLC Number:
Kui FANG, Cheng LI, Xiao HE, Yineng CHEN. Research on Multi-angle Identification of Grape Leaf Disease Based on 3D Reconstruction[J]. Journal of Agricultural Science and Technology, 2022, 24(7): 86-96.
方逵, 李成, 何潇, 陈益能. 基于三维重建的多角度葡萄叶病害识别方法研究[J]. 中国农业科技导报, 2022, 24(7): 86-96.
类别 Category | 初始训练集 Initial training set | 测试集 Test set | 验证集 Validation set | Mag-3D |
---|---|---|---|---|
褐斑病 Brown spot | 123 | 40 | 40 | 7 827 |
气灼病 Gas burning | 55 | 18 | 18 | 3 520 |
健康叶片 Healthy leaf | 116 | 41 | 41 | 7 424 |
缺镁症 Magnesium deficiency | 81 | 27 | 27 | 5 184 |
白粉病 Powdery mildew | 139 | 47 | 47 | 8 896 |
Table 1 Distribution of dataset
类别 Category | 初始训练集 Initial training set | 测试集 Test set | 验证集 Validation set | Mag-3D |
---|---|---|---|---|
褐斑病 Brown spot | 123 | 40 | 40 | 7 827 |
气灼病 Gas burning | 55 | 18 | 18 | 3 520 |
健康叶片 Healthy leaf | 116 | 41 | 41 | 7 424 |
缺镁症 Magnesium deficiency | 81 | 27 | 27 | 5 184 |
白粉病 Powdery mildew | 139 | 47 | 47 | 8 896 |
矩阵Matrix | 预测 Predict | ||
---|---|---|---|
A | B | ||
实际 Reality | A | 真阳性 True positive | 假阴性False negative |
B | 假阳性 False positive | 真阴性True negative |
Table 2 Confusion matrix of single classification
矩阵Matrix | 预测 Predict | ||
---|---|---|---|
A | B | ||
实际 Reality | A | 真阳性 True positive | 假阴性False negative |
B | 假阳性 False positive | 真阴性True negative |
评价指标 Evaluation index | MobileNet | Darknet53 | Resnet34 | Resnet101 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
原模型Original model | 16 | 3D | 原模型Original model | 16 | 3D | 原模型 Original model | 16 | 3D | 原模型 Original model | 16 | 3D | |
准确率 Accuracy/% | 74.9 | 73.7 | 86.2 | 74.9 | 76.0 | 82.6 | 75.4 | 84.4 | 86.8 | 66.5 | 83.2 | 83.8 |
Kappa系数 Kappa coefficient/% | 67.1 | 65.5 | 82.1 | 67.2 | 68.8 | 77.5 | 67.8 | 79.7 | 83.1 | 55.7 | 78.1 | 79.0 |
Table 3 overall recognition effect without data enhancement
评价指标 Evaluation index | MobileNet | Darknet53 | Resnet34 | Resnet101 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
原模型Original model | 16 | 3D | 原模型Original model | 16 | 3D | 原模型 Original model | 16 | 3D | 原模型 Original model | 16 | 3D | |
准确率 Accuracy/% | 74.9 | 73.7 | 86.2 | 74.9 | 76.0 | 82.6 | 75.4 | 84.4 | 86.8 | 66.5 | 83.2 | 83.8 |
Kappa系数 Kappa coefficient/% | 67.1 | 65.5 | 82.1 | 67.2 | 68.8 | 77.5 | 67.8 | 79.7 | 83.1 | 55.7 | 78.1 | 79.0 |
模型Model | 召回率Recall/% | 精确率Precision/% | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | 褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | |
MobileNet | 27.5 | 97.9 | 83.3 | 71.4 | 92.7 | 78.6 | 63.0 | 88.2 | 100.0 | 79.2 |
16-MobileNet | 32.5 | 89.4 | 72.2 | 71.4 | 97.6 | 68.4 | 64.6 | 86.7 | 100.0 | 75.5 |
3D-MobileNet | 65.0 | 95.7 | 88.9 | 81.0 | 97.6 | 92.9 | 80.4 | 88.9 | 94.4 | 85.1 |
Darknet53 | 30.0 | 89.4 | 83.3 | 71.4 | 100.0 | 70.6 | 64.6 | 88.2 | 100.0 | 77.4 |
16-Darknet53 | 37.5 | 85.1 | 77.8 | 85.7 | 97.6 | 68.2 | 66.7 | 93.3 | 100.0 | 76.9 |
3D-Darknet53 | 70.0 | 89.4 | 83.3 | 81.0 | 87.8 | 77.8 | 80.8 | 100.0 | 89.5 | 80.0 |
Resnet34 | 30.0 | 100.0 | 66.7 | 76.2 | 95.1 | 85.7 | 61.0 | 85.7 | 100.0 | 84.8 |
16-Resbet34 | 62.5 | 93.6 | 66.7 | 90.5 | 100.0 | 86. | 80.0 | 100.0 | 100.0 | 78.8 |
3D-resnet34 | 75.0 | 80.9 | 100.0 | 85.7 | 100.0 | 85.7 | 88.4 | 75.0 | 100.0 | 87.2 |
Resnet101 | 17.5 | 97.9 | 61.1 | 52.4 | 87.8 | 87.5 | 52.9 | 68.8 | 100.0 | 80.0 |
16-Resnet101 | 67.5 | 93.6 | 72.2 | 66.7 | 100.0 | 87.1 | 75.9 | 86.7 | 93.3 | 85.4 |
3D-Resnet101 | 67.5 | 91.5 | 88.9 | 66.7 | 97.6 | 84.4 | 81.1 | 80.0 | 93.3 | 85.1 |
Table 4 Single class recognition result without data enhancement
模型Model | 召回率Recall/% | 精确率Precision/% | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | 褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | |
MobileNet | 27.5 | 97.9 | 83.3 | 71.4 | 92.7 | 78.6 | 63.0 | 88.2 | 100.0 | 79.2 |
16-MobileNet | 32.5 | 89.4 | 72.2 | 71.4 | 97.6 | 68.4 | 64.6 | 86.7 | 100.0 | 75.5 |
3D-MobileNet | 65.0 | 95.7 | 88.9 | 81.0 | 97.6 | 92.9 | 80.4 | 88.9 | 94.4 | 85.1 |
Darknet53 | 30.0 | 89.4 | 83.3 | 71.4 | 100.0 | 70.6 | 64.6 | 88.2 | 100.0 | 77.4 |
16-Darknet53 | 37.5 | 85.1 | 77.8 | 85.7 | 97.6 | 68.2 | 66.7 | 93.3 | 100.0 | 76.9 |
3D-Darknet53 | 70.0 | 89.4 | 83.3 | 81.0 | 87.8 | 77.8 | 80.8 | 100.0 | 89.5 | 80.0 |
Resnet34 | 30.0 | 100.0 | 66.7 | 76.2 | 95.1 | 85.7 | 61.0 | 85.7 | 100.0 | 84.8 |
16-Resbet34 | 62.5 | 93.6 | 66.7 | 90.5 | 100.0 | 86. | 80.0 | 100.0 | 100.0 | 78.8 |
3D-resnet34 | 75.0 | 80.9 | 100.0 | 85.7 | 100.0 | 85.7 | 88.4 | 75.0 | 100.0 | 87.2 |
Resnet101 | 17.5 | 97.9 | 61.1 | 52.4 | 87.8 | 87.5 | 52.9 | 68.8 | 100.0 | 80.0 |
16-Resnet101 | 67.5 | 93.6 | 72.2 | 66.7 | 100.0 | 87.1 | 75.9 | 86.7 | 93.3 | 85.4 |
3D-Resnet101 | 67.5 | 91.5 | 88.9 | 66.7 | 97.6 | 84.4 | 81.1 | 80.0 | 93.3 | 85.1 |
评价指标 Evaluation index | MobileNet | Darknet53 | Resnet34 | Resnet101 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
原模型Original model | 16 | 3D | 原模型Original model | 16 | 3D | 原模型 Original model | 16 | 3D | 原模型 Original model | 16 | 3D | |
准确率 Accuracy/% | 78.4 | 86.8 | 89.2 | 82.0 | 81.4 | 84.4 | 80.8 | 85.6 | 89.2 | 74.3 | 84.4 | 85.0 |
Kappa系数 Kappa coefficient/% | 71.7 | 82.9 | 86.0 | 76.5 | 75.8 | 79.8 | 75.0 | 81.4 | 86.1 | 65.9 | 79.8 | 80.7 |
Table 5 Overall recognition effect under geometric change mode
评价指标 Evaluation index | MobileNet | Darknet53 | Resnet34 | Resnet101 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
原模型Original model | 16 | 3D | 原模型Original model | 16 | 3D | 原模型 Original model | 16 | 3D | 原模型 Original model | 16 | 3D | |
准确率 Accuracy/% | 78.4 | 86.8 | 89.2 | 82.0 | 81.4 | 84.4 | 80.8 | 85.6 | 89.2 | 74.3 | 84.4 | 85.0 |
Kappa系数 Kappa coefficient/% | 71.7 | 82.9 | 86.0 | 76.5 | 75.8 | 79.8 | 75.0 | 81.4 | 86.1 | 65.9 | 79.8 | 80.7 |
模型Model | 召回率Recall/% | 精确率Precision/% | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | 褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | |
MobileNet | 45.0 | 97.9 | 61.1 | 76.2 | 97.6 | 45.0 | 97.9 | 61.1 | 76.2 | 97.6 |
16-MobileNet | 70.0 | 93.6 | 83.3 | 85.7 | 97.6 | 70.0 | 93.6 | 83.3 | 85.7 | 97.6 |
3D-MobileNet | 80.0 | 89.4 | 94.4 | 85.7 | 97.6 | 80.0 | 89.4 | 94.4 | 85.7 | 97.6 |
Darknet53 | 57.5 | 95.7 | 83.3 | 71.4 | 95.1 | 57.5 | 95.7 | 83.3 | 71.4 | 95.1 |
16-Darknet53 | 70.0 | 87.2 | 83.3 | 71.4 | 90.2 | 70.0 | 87.2 | 83.3 | 71.4 | 90.2 |
3D-Darknet53 | 62.5 | 95.7 | 83.3 | 81.0 | 95.1 | 62.5 | 95.7 | 83.3 | 81.0 | 95.1 |
Resnet34 | 50.0 | 91.5 | 72.2 | 85.7 | 100.0 | 50.0 | 91.5 | 72.2 | 85.7 | 100.0 |
16-Resbet34 | 80.0 | 83.0 | 88.9 | 81.0 | 95.1 | 80.0 | 83.0 | 88.9 | 81.0 | 95.1 |
3D-resnet34 | 82.5 | 85.1 | 94.4 | 90.5 | 97.6 | 82.5 | 85.1 | 94.4 | 90.5 | 97.6 |
Resnet101 | 45.0 | 100.0 | 33.3 | 76.2 | 90.2 | 45.0 | 100.0 | 33.3 | 76.2 | 90.2 |
16-Resnet101 | 70.0 | 93.6 | 94.4 | 71.4 | 90.2 | 70.0 | 93.6 | 94.4 | 71.4 | 90.2 |
3D-Resnet101 | 72.5 | 78.7 | 94.4 | 85.7 | 100.0 | 72.5 | 78.7 | 94.4 | 85.7 | 100.0 |
Table 6 Single category recognition result under geometric change mode
模型Model | 召回率Recall/% | 精确率Precision/% | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | 褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | |
MobileNet | 45.0 | 97.9 | 61.1 | 76.2 | 97.6 | 45.0 | 97.9 | 61.1 | 76.2 | 97.6 |
16-MobileNet | 70.0 | 93.6 | 83.3 | 85.7 | 97.6 | 70.0 | 93.6 | 83.3 | 85.7 | 97.6 |
3D-MobileNet | 80.0 | 89.4 | 94.4 | 85.7 | 97.6 | 80.0 | 89.4 | 94.4 | 85.7 | 97.6 |
Darknet53 | 57.5 | 95.7 | 83.3 | 71.4 | 95.1 | 57.5 | 95.7 | 83.3 | 71.4 | 95.1 |
16-Darknet53 | 70.0 | 87.2 | 83.3 | 71.4 | 90.2 | 70.0 | 87.2 | 83.3 | 71.4 | 90.2 |
3D-Darknet53 | 62.5 | 95.7 | 83.3 | 81.0 | 95.1 | 62.5 | 95.7 | 83.3 | 81.0 | 95.1 |
Resnet34 | 50.0 | 91.5 | 72.2 | 85.7 | 100.0 | 50.0 | 91.5 | 72.2 | 85.7 | 100.0 |
16-Resbet34 | 80.0 | 83.0 | 88.9 | 81.0 | 95.1 | 80.0 | 83.0 | 88.9 | 81.0 | 95.1 |
3D-resnet34 | 82.5 | 85.1 | 94.4 | 90.5 | 97.6 | 82.5 | 85.1 | 94.4 | 90.5 | 97.6 |
Resnet101 | 45.0 | 100.0 | 33.3 | 76.2 | 90.2 | 45.0 | 100.0 | 33.3 | 76.2 | 90.2 |
16-Resnet101 | 70.0 | 93.6 | 94.4 | 71.4 | 90.2 | 70.0 | 93.6 | 94.4 | 71.4 | 90.2 |
3D-Resnet101 | 72.5 | 78.7 | 94.4 | 85.7 | 100.0 | 72.5 | 78.7 | 94.4 | 85.7 | 100.0 |
评价指标 Evaluation index | MobileNet | Darknet53 | Resnet34 | Resnet101 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
原模型Original model | 16 | 3D | 原模型Original model | 16 | 3D | 原模型 Original model | 16 | 3D | 原模型 Original model | 16 | 3D | |
准确率 Accuracy/% | 83.8 | 87.4 | 91.0 | 80.2 | 87.4 | 89.8 | 79.6 | 87.4 | 89.8 | 74.3 | 89.8 | 93.4 |
Kappa系数 Kappa coefficient/% | 78.9 | 83.7 | 88.4 | 74.2 | 83.7 | 86.8 | 73.3 | 83.7 | 86.9 | 66.0 | 86.9 | 91.5 |
Table 7 Overall recognition effect under geometric and color changes
评价指标 Evaluation index | MobileNet | Darknet53 | Resnet34 | Resnet101 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
原模型Original model | 16 | 3D | 原模型Original model | 16 | 3D | 原模型 Original model | 16 | 3D | 原模型 Original model | 16 | 3D | |
准确率 Accuracy/% | 83.8 | 87.4 | 91.0 | 80.2 | 87.4 | 89.8 | 79.6 | 87.4 | 89.8 | 74.3 | 89.8 | 93.4 |
Kappa系数 Kappa coefficient/% | 78.9 | 83.7 | 88.4 | 74.2 | 83.7 | 86.8 | 73.3 | 83.7 | 86.9 | 66.0 | 86.9 | 91.5 |
模型Model | 召回率Recall/% | 精确率Precision/% | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | 褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | |
MobileNet | 62.5 | 95.7 | 66.7 | 81.0 | 100.0 | 86.2 | 73.8 | 100.0 | 94.4 | 87.2 |
16-MobileNet | 82.5 | 80.9 | 77.8 | 100.0 | 97.6 | 71.7 | 90.5 | 100.0 | 100.0 | 90.9 |
3D-MobileNet | 87.5 | 85.1 | 94.4 | 95.2 | 97.6 | 83.3 | 88.9 | 94.4 | 100.0 | 95.2 |
Darknet53 | 52.5 | 95.7 | 83.3 | 71.4 | 92.7 | 87.5 | 69.2 | 88.2 | 100.0 | 82.6 |
16-Darknet53 | 87.5 | 80.9 | 88.9 | 81.0 | 97.6 | 74.5 | 92.7 | 100.0 | 100.0 | 87.0 |
3D-Darknet53 | 75.0 | 89.4 | 94.4 | 95.2 | 100.0 | 83.3 | 87.5 | 100.0 | 100.0 | 89.1 |
Resnet34 | 50.0 | 97.9 | 66.7 | 81.0 | 92.7 | 95.2 | 62.2 | 100.0 | 100.0 | 88.4 |
16-Resbet34 | 85.0 | 83.0 | 77.8 | 85.7 | 100.0 | 75.6 | 90.7 | 100.0 | 94.7 | 89.1 |
3D-resnet34 | 80.0 | 89.4 | 94.4 | 95.2 | 95.1 | 88.9 | 87.5 | 85.0 | 95.2 | 92.9 |
Resnet101 | 25.0 | 97.9 | 61.1 | 81.0 | 97.6 | 90.9 | 56.8 | 100.0 | 100.0 | 85.1 |
16-Resnet101 | 90.0 | 83.0 | 94.4 | 85.7.0 | 97.6 | 78.3 | 95.1 | 100.0 | 100.0 | 88.9 |
3D-Resnet101 | 92.5 | 89.4 | 88.9 | 100.0 | 97.6 | 86.0 | 95.5 | 100.0 | 95.5 | 95.2 |
Table 8 Single class recognition result under geometric and color change modes
模型Model | 召回率Recall/% | 精确率Precision/% | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | 褐斑病 Brown spot | 白粉病 Powdery mildew | 气灼病 Gas burning | 缺镁症 Magnesium deficiency | 健康 Health | |
MobileNet | 62.5 | 95.7 | 66.7 | 81.0 | 100.0 | 86.2 | 73.8 | 100.0 | 94.4 | 87.2 |
16-MobileNet | 82.5 | 80.9 | 77.8 | 100.0 | 97.6 | 71.7 | 90.5 | 100.0 | 100.0 | 90.9 |
3D-MobileNet | 87.5 | 85.1 | 94.4 | 95.2 | 97.6 | 83.3 | 88.9 | 94.4 | 100.0 | 95.2 |
Darknet53 | 52.5 | 95.7 | 83.3 | 71.4 | 92.7 | 87.5 | 69.2 | 88.2 | 100.0 | 82.6 |
16-Darknet53 | 87.5 | 80.9 | 88.9 | 81.0 | 97.6 | 74.5 | 92.7 | 100.0 | 100.0 | 87.0 |
3D-Darknet53 | 75.0 | 89.4 | 94.4 | 95.2 | 100.0 | 83.3 | 87.5 | 100.0 | 100.0 | 89.1 |
Resnet34 | 50.0 | 97.9 | 66.7 | 81.0 | 92.7 | 95.2 | 62.2 | 100.0 | 100.0 | 88.4 |
16-Resbet34 | 85.0 | 83.0 | 77.8 | 85.7 | 100.0 | 75.6 | 90.7 | 100.0 | 94.7 | 89.1 |
3D-resnet34 | 80.0 | 89.4 | 94.4 | 95.2 | 95.1 | 88.9 | 87.5 | 85.0 | 95.2 | 92.9 |
Resnet101 | 25.0 | 97.9 | 61.1 | 81.0 | 97.6 | 90.9 | 56.8 | 100.0 | 100.0 | 85.1 |
16-Resnet101 | 90.0 | 83.0 | 94.4 | 85.7.0 | 97.6 | 78.3 | 95.1 | 100.0 | 100.0 | 88.9 |
3D-Resnet101 | 92.5 | 89.4 | 88.9 | 100.0 | 97.6 | 86.0 | 95.5 | 100.0 | 95.5 | 95.2 |
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