Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method

Liu, Linyi; Dong, Yingying; Huang, Wenjiang; Du, Xiaoping; Luo, Jiancheng; Shi, Yue; Ma, Huiqin

doi:10.3390/rs11030298

Cited by 22 publications

(11 citation statements)

References 59 publications

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“…The small plot size led to the lack of a model validation process based on real independent data. Limited training data are a common problem in remote sensing applications [56], and many approaches have been used to mitigate small training samples, including data augmentation, unsupervised training, and transfer learning [69]. Future research could attempt to use these methods to overcome the problem of small plots and thus develop a more stable and efficient monitoring model.…”

Section: Discussionmentioning

confidence: 99%

Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery

et al. 2020

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The monitoring of winter wheat Fusarium head blight via rapid and non-destructive measures is important for agricultural production and disease control. Images of unmanned aerial vehicles (UAVs) are particularly suitable for the monitoring of wheat diseases because they feature high spatial resolution and flexible acquisition time. This study evaluated the potential to monitor Fusarium head blight via UAV hyperspectral imagery. The field site investigated by this study is located in Lujiang County, Anhui Province, China. The hyperspectral UAV images were acquired on 3 and 8 May 2019, when wheat was at the grain filling stage. Several features, including original spectral bands, vegetation indexes, and texture features, were extracted from these hyperspectral images. Based on these extracted features, univariate Fusarium monitoring models were developed, and backward feature selection was applied to filter these features. The backpropagation (BP) neural network was improved by integrating a simulated annealing algorithm in the experiment. A multivariate Fusarium head blight monitoring model was developed using the improved BP neural network. The results showed that bands in the red region provide important information for discriminating between wheat canopies that are either slightly or severely Fusarium-head-blight-infected. The modified chlorophyll absorption reflectance index performed best among all features, with an area under the curve and standard deviation of 1.0 and 0.0, respectively. Five commonly used methods were compared with this improved BP neural network. The results showed that the developed Fusarium head blight monitoring model achieved the highest overall accuracy of 98%. In addition, the difference between the producer accuracy and user accuracy of the improved BP neural network was smallest among all models, indicating that this model achieved better stability. These results demonstrate that hyperspectral images of UAVs can be used to monitor Fusarium head blight in winter wheat.

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Section: Discussionmentioning

confidence: 99%

Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery

et al. 2020

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“…As an effective method in object detection over large areas, satellite-based remote sensing technology has become a more viable option for monitoring crop diseases [5]. Since changes in morphology, leaf color, chlorosis and transpiration rate of infected plants can be directly extracted from radiometric measurements, crop diseases can be monitored and identified by remote sensing [6].…”

Section: Graminearum (Gibberella Zeae)mentioning

confidence: 99%

A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery

et al. 2020

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Rapid, non-destructive detection of wheat Fusarium head blight (FHB) is an important tool for disease control. Red-edge (RE) is a prominent spectral feature for determining crop conditions with the potential to enhance the accuracy of monitoring FHB regionally. This study explored the potential of RE for FHB monitoring based on Sentinel-2 Multispectral Instrument (MSI) data. The novel red-edge head blight index (REHBI) was developed to detect FHB at a regional scale. Hyperspectral data at the canopy scale was integrated to simulate Sentinel-2 multispectral reflectance using the relative spectral response (RSR) function of the sensor. Then, many differential and ratio combinations of Sentinel-2 bands that were sensitive to FHB severity were selected. REHBI was established based on these basic vegetation indexes (VIs), and the model developed from REHBI performed best in monitoring FHB severity (R 2 = 0.82, RMSE = 10.1). Additionally, the infected canopies with disease index (DI) values between 10 and 50 were classified as slightly diseased canopies. Ordinary least square (OLS) was used to test the performance of REHBI and two conventional VIs, i.e., OSAVI and RDVI, in monitoring slightly diseased canopies; REHBI outperformed these alternatives (R 2 = 0.69, RMSE = 3.6). To approximate real agricultural conditions, Poisson noise was added to the simulated Sentinel-2 multispectral data and generalized performance of VIs was evaluated again; REHBI still had the highest R 2 and lowest RMSE values (0.74 and 12.6, respectively). Finally, to validate REHBI's ability to detect FHB infection in agricultural production, it was applied to monitoring FHB in the wheat planting areas of Changfeng and Dingyuan counties from Sentinel-2 imagery. Generally, REHBI performed better in disease monitoring than OSAVI and RDVI. The overall accuracy was up to 78.6%, and the kappa coefficient was 0.51. Experimental results demonstrate that REHBI can be used to monitor FHB. INDEX TERMS Red-edge, wheat fusarium head blight, Sentinel-2, spectral analysis, disease index.

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“…Winter wheat is usually planted in early October and harvested in the middle of June [39]. The major soil type in this region is reported to be Haplic Luvisols [40]. Suitable temperature and abundant sunshine make it suitable for crop growth.…”

Section: Study Sitementioning

confidence: 99%

Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

Huang

Jing

et al. 2019

Remote Sensing

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Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest.

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Enhanced Regional Monitoring of Wheat Powdery Mildew Based on an Instance-Based Transfer Learning Method

Cited by 22 publications

References 59 publications

Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery

Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery

A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery

Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

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