Journal of Agricultural Science and Technology ›› 2023, Vol. 25 ›› Issue (8): 126-137.DOI: 10.13304/j.nykjdb.2022.0866
• INTELLIGENT AGRICULTURE & AGRICULTURAL MACHINERY • Previous Articles
Fengxia BIAN1(), Kaige LIU2, Xinmin RONG1(
)
Received:
2022-10-12
Accepted:
2022-12-29
Online:
2023-08-20
Published:
2023-09-07
Contact:
Xinmin RONG
通讯作者:
容新民
作者简介:
边凤霞 E-mail:bianfengxia123@163.com;
基金资助:
CLC Number:
Fengxia BIAN, Kaige LIU, Xinmin RONG. Development and Verification of Prediction Model for Grape Downy Mildew Based on Machine Learning[J]. Journal of Agricultural Science and Technology, 2023, 25(8): 126-137.
边凤霞, 刘凯歌, 容新民. 基于机器学习构建葡萄霜霉病预测模型及验证[J]. 中国农业科技导报, 2023, 25(8): 126-137.
Fig. 2 Decision tree model principleNote: The schematic diagram only shows the decision-making process of the model when dealing with the problem, and does not mean that the decision result of a single feature is used as the prediction result.
指标 Index | 样本数Number of samples | |
---|---|---|
预测不发病 Prediction label: 0 | 预测发病Prediction label: 1 | |
实际不发病 True label: 0 | 真阴性 True negative: TN | 假阳性 False positive: FP |
实际发病 True label: 1 | 假阴性 False negative:FN | 真阳性 True positive:TP |
Table 1 Confusion matrix contingency table
指标 Index | 样本数Number of samples | |
---|---|---|
预测不发病 Prediction label: 0 | 预测发病Prediction label: 1 | |
实际不发病 True label: 0 | 真阴性 True negative: TN | 假阳性 False positive: FP |
实际发病 True label: 1 | 假阴性 False negative:FN | 真阳性 True positive:TP |
k值 k value | 一致性结果 Consistent result |
---|---|
0≤k<0.2 | 极低Slight |
0.2≤k<0.4 | 一般Fair |
0.4≤k<0.6 | 中等Moderate |
0.6≤k<0.8 | 高度Substantial |
0.8≤k<1.0 | 几乎完全Almost perfect |
Table 2 The k value consistency classification criteria
k值 k value | 一致性结果 Consistent result |
---|---|
0≤k<0.2 | 极低Slight |
0.2≤k<0.4 | 一般Fair |
0.4≤k<0.6 | 中等Moderate |
0.6≤k<0.8 | 高度Substantial |
0.8≤k<1.0 | 几乎完全Almost perfect |
模型 Model | 类别标签 Classe label | 分类结果 Classification result | |||
---|---|---|---|---|---|
准确率 Accuracy/% | 精准率 Precision/% | 召回率 Recall/% | F1分值 F1-score/% | ||
BLR | 0 | 86 | 87 | 93 | 90 |
1 | 83 | 70 | 76 | ||
SVM | 0 | 86 | 86 | 94 | 90 |
1 | 85 | 69 | 76 | ||
DT | 0 | 94 | 95 | 96 | 95 |
1 | 91 | 90 | 91 | ||
KNN | 0 | 93 | 96 | 94 | 95 |
1 | 88 | 91 | 89 |
Table 3 Classification results of different prediction models
模型 Model | 类别标签 Classe label | 分类结果 Classification result | |||
---|---|---|---|---|---|
准确率 Accuracy/% | 精准率 Precision/% | 召回率 Recall/% | F1分值 F1-score/% | ||
BLR | 0 | 86 | 87 | 93 | 90 |
1 | 83 | 70 | 76 | ||
SVM | 0 | 86 | 86 | 94 | 90 |
1 | 85 | 69 | 76 | ||
DT | 0 | 94 | 95 | 96 | 95 |
1 | 91 | 90 | 91 | ||
KNN | 0 | 93 | 96 | 94 | 95 |
1 | 88 | 91 | 89 |
模型 Model | 类别标签 Classe label | 分类结果 Classification result | |||
---|---|---|---|---|---|
准确率 Accuracy/% | 精准率 Precision/% | 召回率 Recall/% | F1分值 F1-score/% | ||
BLR | 0 | 73 | 73 | 87 | 80 |
1 | 74 | 53 | 62 | ||
SVM | 0 | 75 | 76 | 86 | 81 |
1 | 72 | 59 | 65 | ||
DT | 0 | 90 | 92 | 91 | 92 |
1 | 87 | 88 | 88 | ||
KNN | 0 | 84 | 86 | 87 | 86 |
1 | 80 | 79 | 80 |
Table 4 Classification validation results of different prediction models
模型 Model | 类别标签 Classe label | 分类结果 Classification result | |||
---|---|---|---|---|---|
准确率 Accuracy/% | 精准率 Precision/% | 召回率 Recall/% | F1分值 F1-score/% | ||
BLR | 0 | 73 | 73 | 87 | 80 |
1 | 74 | 53 | 62 | ||
SVM | 0 | 75 | 76 | 86 | 81 |
1 | 72 | 59 | 65 | ||
DT | 0 | 90 | 92 | 91 | 92 |
1 | 87 | 88 | 88 | ||
KNN | 0 | 84 | 86 | 87 | 86 |
1 | 80 | 79 | 80 |
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