基于时间序列植被指数的小麦条锈病抗性等级鉴定方法
S127%S24; 条锈病严重影响小麦产量,培育抗条锈病的小麦品种至关重要.针对传统育种中抗性鉴定手段单一、效率低的问题,该研究提出了一种通过小麦冠层植被指数的时间序列实现对条锈病不同抗性等级的高效鉴定方法.该方法利用无人机采集自然发病的育种群体小麦(共 600 个样本,516 个基因型)冠层多时相的光谱图像,使用随机蛙跳算法和ReliefF算法筛选出 6个条锈病病害严重度的敏感特征:归一化色素叶绿素指数(normalized pigment chlorophyll index,NPCI)、沃尔贝克指数(woebbecke index,WI)、叶绿素红边指数(chlorophyll index...
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| Published in | 农业工程学报 Vol. 40; no. 4; pp. 155 - 165 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
陕西省农业信息感知与智能服务重点实验室,杨凌 712100%西北农林科技大学农学院,杨凌 712100
01.02.2024
西北农林科技大学旱区作物逆境生物学国家重点实验室,杨凌 712100 西北农林科技大学机械与电子工程学院,杨凌 712100 农业农村部农业物联网重点实验室,杨凌 712100 |
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| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.202311040 |
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| Abstract | S127%S24; 条锈病严重影响小麦产量,培育抗条锈病的小麦品种至关重要.针对传统育种中抗性鉴定手段单一、效率低的问题,该研究提出了一种通过小麦冠层植被指数的时间序列实现对条锈病不同抗性等级的高效鉴定方法.该方法利用无人机采集自然发病的育种群体小麦(共 600 个样本,516 个基因型)冠层多时相的光谱图像,使用随机蛙跳算法和ReliefF算法筛选出 6个条锈病病害严重度的敏感特征:归一化色素叶绿素指数(normalized pigment chlorophyll index,NPCI)、沃尔贝克指数(woebbecke index,WI)、叶绿素红边指数(chlorophyll index rededge,CIrededge)、绿大气抵抗植被指数(green atmospherically resistant index,GARI)、归一化差分植被指数(normalized difference vi,NDVI)、叶绿素绿指数(chlorophyll index green,CIgreen),这些敏感特征在试验群体中的时间序列符合条锈病的发病规律,验证了其作为条锈病发病严重度敏感特征的有效性;基于支持向量机(support vector machine,SVM)算法使用上述敏感特征建立条锈病病害严重度等级分类模型,在测试集的表现中,与使用未经过筛选的原始特征所建立的模型相比在精度、平均准确率、平均召回率和F1分数上分别仅下降 6.2%、3.3%、2.7%、4.0%,证明了所筛选敏感特征的有效性;针对一般机器学习算法难以捕捉不同抗性等级样本之间较小的特征变化差异的问题,提出了一种从植被指数时间序列转化生成的二维图像中提取特征实现条锈病抗性等级分类的方法.将敏感特征中能够较好区分不同抗病等级的 4个时间序列植被指数(NPCI、GARI、NDVI、WI),通过格拉姆角场方法生成格拉姆角和场图像,并制作成数据集,使用DenseNet121网络进行训练,以实现不同条锈病抗病等级的分类.建立的条锈病抗性等级分类模型中,由NPCI时间序列图像建立的分类模型测试效果最佳,其准确率为 0.837,召回率为 0.834,F1分数可达 0.833,能够较好地实现对群体小麦不同品种(系)的条锈病抗性等级差异的区分,表明基于光谱植被指数时间序列的小麦条锈病抗性等级识别方法可以用于小麦抗病育种中抗性等级的鉴定,并可为其他作物的病害抗性等 |
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| AbstractList | S127%S24; 条锈病严重影响小麦产量,培育抗条锈病的小麦品种至关重要.针对传统育种中抗性鉴定手段单一、效率低的问题,该研究提出了一种通过小麦冠层植被指数的时间序列实现对条锈病不同抗性等级的高效鉴定方法.该方法利用无人机采集自然发病的育种群体小麦(共 600 个样本,516 个基因型)冠层多时相的光谱图像,使用随机蛙跳算法和ReliefF算法筛选出 6个条锈病病害严重度的敏感特征:归一化色素叶绿素指数(normalized pigment chlorophyll index,NPCI)、沃尔贝克指数(woebbecke index,WI)、叶绿素红边指数(chlorophyll index rededge,CIrededge)、绿大气抵抗植被指数(green atmospherically resistant index,GARI)、归一化差分植被指数(normalized difference vi,NDVI)、叶绿素绿指数(chlorophyll index green,CIgreen),这些敏感特征在试验群体中的时间序列符合条锈病的发病规律,验证了其作为条锈病发病严重度敏感特征的有效性;基于支持向量机(support vector machine,SVM)算法使用上述敏感特征建立条锈病病害严重度等级分类模型,在测试集的表现中,与使用未经过筛选的原始特征所建立的模型相比在精度、平均准确率、平均召回率和F1分数上分别仅下降 6.2%、3.3%、2.7%、4.0%,证明了所筛选敏感特征的有效性;针对一般机器学习算法难以捕捉不同抗性等级样本之间较小的特征变化差异的问题,提出了一种从植被指数时间序列转化生成的二维图像中提取特征实现条锈病抗性等级分类的方法.将敏感特征中能够较好区分不同抗病等级的 4个时间序列植被指数(NPCI、GARI、NDVI、WI),通过格拉姆角场方法生成格拉姆角和场图像,并制作成数据集,使用DenseNet121网络进行训练,以实现不同条锈病抗病等级的分类.建立的条锈病抗性等级分类模型中,由NPCI时间序列图像建立的分类模型测试效果最佳,其准确率为 0.837,召回率为 0.834,F1分数可达 0.833,能够较好地实现对群体小麦不同品种(系)的条锈病抗性等级差异的区分,表明基于光谱植被指数时间序列的小麦条锈病抗性等级识别方法可以用于小麦抗病育种中抗性等级的鉴定,并可为其他作物的病害抗性等 |
| Abstract_FL | Stripe rust has posed a serious threat to the wheat yield in recent years.It is crucial to breed the wheat varieties resistant to stripe rust.However,the identification of resistance is single and inefficient in traditional breeding.In this study,an efficient identification was proposed to determine the different resistance grades to the stripe rust using the time series of vegetation index response to wheat canopy.An unmanned aerial vehicle(UAV)was utilized to collect multi-temporal spectral images of the canopy in the naturally occurring breeding populations of colony wheat(600 samples in total,516 genotypes).Six sensitive features were screened for the severity of stripe rust disease using Random Forest and ReliefF algorithms:normalized pigment chlorophyll index(NPCI),woebbecke index(WI),chlorophyll index rededge(CIrededge),(green atmospherically resistant index GARI),normalized difference vi(NDVI),and chlorophyll index green(CIgreen).These indices were verified as sensitive features.The severity of stripe rust disease incidence was dynamically characterized using the time series of these indices in the test population.The support vector machine(SVM)was used to establish a classification model for the severity grade of stripe rust disease,according to the sensitive features.There was a very small difference in the performance of the test set and the unscreened original features,indicating the effectiveness of the screened sensitive features.The time series of six sensitive traits was observed in the samples of different resistance grades.It was found that there were no significant differences in the CIgreen and CIrededge among the samples with the different resistance grades.This indicated that the saamples were not applicable to categorize the resistance grades to stripe rust.The differences exhibited by GARI,NDVI,NPCI and WI were used to classify the resistance grades to stripe rust.General machine learning cannot capture the smaller differences of feature variation in the samples with the different resistance grades.Therefore,an improved mode was proposed to extract the features from two-dimensional images that transformed vegetation index time series,in order to realize the classification of stripe rust resistance grade.Four time-series vegetation indices(NPCI,GARI,NDVI,and WI)were better distinguished the different disease resistance grades among the sensitive features,and then used to generate the Gramian Angular Summation Field(GASF)images by the Gramian Angular Field.Data augmentation was performed on the dataset to equalize the number of samples in each resistance grade.Each dataset had a total of 1 040 samples,and was then divided into four grades of stripe rust resistance,where each grade contained 260 sample images,while each dataset was divided into training,validation,and testing sets in the ratio of 6:2:2.DenseNet121 model was separately trained using each dataset,in order to classify the various stripe rust resistance.A better performance was achieved in the classification models with the GASF_NPCI and GASF_WI as the input features,compared with the GASF_GARI and GASF_NDVI.The model with the GASF_NPCI as a feature was slightly less effective in distinguishing the samples with the resistance grades R and MR,where the precision and recall were relatively low.There was no difference in the models with the GASF_WI for the precision and recall of the samples that predicted each stripe rust resistance grade.In the F1 scores of the test set,the different vegetation indices on the resistance grades of stripe rust in colony wheat were ranked in the order of NPCI,WI,GARI,NDVI.The classification model with the GASF_NPCI was the most effective in the test set,with an F1 score of up to 0.833.There was a better distinction of differences in the stripe rust resistance grades among different varieties(lines)of population wheat.The grades of wheat stripe rust resistance were fully identified using time series of spectral vegetation index.Meanwhile,the finding can also provide a strong reference for the disease resistance breeding of crops. |
| Author | 陈启帆 韩德俊 吴建辉 苏宝峰 刘砥柱 |
| AuthorAffiliation | 西北农林科技大学机械与电子工程学院,杨凌 712100;农业农村部农业物联网重点实验室,杨凌 712100;陕西省农业信息感知与智能服务重点实验室,杨凌 712100%西北农林科技大学农学院,杨凌 712100;西北农林科技大学旱区作物逆境生物学国家重点实验室,杨凌 712100 |
| AuthorAffiliation_xml | – name: 西北农林科技大学机械与电子工程学院,杨凌 712100;农业农村部农业物联网重点实验室,杨凌 712100;陕西省农业信息感知与智能服务重点实验室,杨凌 712100%西北农林科技大学农学院,杨凌 712100;西北农林科技大学旱区作物逆境生物学国家重点实验室,杨凌 712100 |
| Author_FL | SU Baofeng LIU Dizhu CHEN Qifan HAN Dejun WU Jianhui |
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| DocumentTitle_FL | Method for the identification of wheat stripe rust resistance grade using time series vegetation index |
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| Keywords | multispectral imaging unmanned aerial vehicle 多光谱成像 深度学习 无人机 remote sensing 遥感 machine learning DenseNet121 deep learning 机器学习 wheat stripe rust 小麦条锈病 |
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| Snippet | S127%S24; 条锈病严重影响小麦产量,培育抗条锈病的小麦品种至关重要.针对传统育种中抗性鉴定手段单一、效率低的问题,该研究提出了一种通过小麦冠层植被指数的时间序列实现... |
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| Title | 基于时间序列植被指数的小麦条锈病抗性等级鉴定方法 |
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