Crop Disease and Pest Monitoring by Remote Sensing

Huang, Wenjiang; Luo, Jiancheng; Zhang, Jingcheng; Zhao, Jing; Zhao, Chunjiang; Wang, Jihua; Yang, Guijun; Muyi, Huang; Huang, Liusheng; Du, Shiqiang

doi:10.5772/35204

Cited by 30 publications

(29 citation statements)

References 42 publications

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“…For example, 417 nm, 658 nm, and 789 nm were selected when distinguishing different background factors; and it does not appear in the recognizing different disease severities. However, the data in the Table 5 and 6 fully present a close relationship between red-edge spectral response and disease stress, which is consistent with other research results in the crop disease diagnosis (Devadas et al, 2009;Zhang et al, 2012).…”

Section: Recognition Featuressupporting

confidence: 80%

“…Fig. 2 shows significant spectral differences for the four measured targets in visible region of 400-700 nm, especially in the green and red bands (Zhang et al, 2012). Despite the shadowed leaf has the spectral response of vegetation, its magnitude is significantly lower than the other three, and the green peak of 550 nm is not prominent.…”

Section: Spectral Response Of Different Background Factorsmentioning

confidence: 94%

“…Thus, the spectral responses of infected vegetation should be expressed as spectral features through certain data processing. This is the fundamental basis of optical remote sensing for assessment of disease severities of vegetation (Devadas et al, 2009;Zhang et al, 2012). Fig.…”

Section: Spectral Response Of Different Background Factorsmentioning

confidence: 99%

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Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging

Zhang¹,

Lin²,

Huang³

et al. 2016

IJAB

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Accurate assessment of crop disease severities is the key for precision application of pesticides to prevent disease infestation. In-situ hyperspectral imaging technology can provide high-resolution imagery with spectra for rapid identification of crop disease and determining disease infestation trend. In this study a hyperspectral imager was used to detect wheat powdery mildew with considering the impacts of wheat ears and the leaves under shadow to identify infected and healthy plant leaves. Through comparing the spectral differences between wheat ears and shadowed, healthy and infected plant leaves, 23 sensitive bands were chosen to distinguish different background targets. Five vegetation indices (VIs) and three red edge parameters were calculated based on screened sensitive bands. Then, 40 identification features were determined to distinguish different background factors and disease severities. Moreover, the classification and regression tree (CRT) was utilized to develop the prediction model of wheat powdery mildew. The identification accuracy was assessed by cross-validation with the accuracies that shadowed leaves can be perfectly recognized while the healthy and infected leaves, wheat ears could be identified with the rates of 98.4, 98.4 and 80.8%, respectively. For identification of different disease severities, the healthy leaves have the highest accuracy with 99.2%, while moderately and mildly infected leaves were determined as 88.2 and 87.8%, respectively. In overall, it was found that wheat ears could affect identification accuracy of wheat powdery mildew. At the same time, in order to provide guidance for application of pesticides, improved accuracy for detecting mildly infected disease is expected.

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Section: Recognition Featuressupporting

confidence: 80%

Section: Spectral Response Of Different Background Factorsmentioning

confidence: 94%

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Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging

Zhang¹,

Lin²,

Huang³

et al. 2016

IJAB

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“…e content and activity of the pigments are reduced, resulting in an increase in the reflectance of the visible region. Moreover, the water metabolism of infected plants will be disturbed, which will cause the water deficit of the leaves to some extent, and finally cause the change of reflectance in the near-infrared region [32].…”

Section: Spectral Response Characteristics and Correlationmentioning

confidence: 99%

Monitoring of Wheat Scab Using the Specific Spectral Index from ASD Hyperspectral Dataset

Huang

Zhang

Ding

et al. 2019

Journal of Spectroscopy

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It is highly important to accurately monitor wheat scab and provide technical guidance for the crop pests and diseases. In this study, relevant analysis was performed among spectral reflectance, first-derivate data, and the disease severity data through ASD hyperspectral data. Two sensitive spectral wavelength ranges of 450–488 nm and 500–540 nm were selected. Then, a new wheat scab index (WSI) consisting of the two bands was proposed. The inversion models of the scab severities were comparatively built by unitary linear regression and multiple stepwise regression techniques. The results showed that the WSI had a significant linear relationship with severity of disease compared with other commonly used spectral indices. The fitting R2, testing R2, and RMSE were 0.73, 0.70, and 13.41, respectively. The multiple stepwise regression model established using the WSI, SDg/SDb, NBNDVI, and SDg as independent variables was better than the single-variable model. Our results suggest that WSI can be used to provide scientific guidance for monitoring and precise management of wheat scab disease.

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“…When crop diseases and pests occur, the chlorophyll level will appear different degrees of decline. The decline is usually expressed as a reflectance decrease in the near-infrared band, which can be detected in the near-infrared remote sensing images [20]. The reflectance spectral curve of crop diseases and pests is as shown in Figure 20.…”

Section: Crop Diseases and Pests Monitoringmentioning

confidence: 99%

UAV Remote Sensing Image Mosaic and Its Application in Agriculture

Jia¹,

Su²,

Shen³

et al. 2016

IJSH

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UAV remote sensing image has the characteristics of higher spatial resolution, fine timeliness and high flexibility. It is widely used in many fields such as agriculture, forestry, soil resources and so on. Especially in agriculture, it plays an important role in information acquisition of agricultural production and agricultural condition monitoring. In the experiment, high-definition digital camera and multi-spectral camera are used to capture the visible light and near-infrared remote sensing image. Two pieces of 3m*3m calibration cloth is used for radiometric calibration and SIFT algorithm was used for image mosaic. This paper discusses the application of remote sensing image in agriculture, including rice lodging monitoring, diseases and pests monitoring, crop growth monitoring and crop nutrient diagnosis. The results show that UAV remote sensing provides an effective means for agricultural condition information acquisition, and has wide application prospect.

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Crop Disease and Pest Monitoring by Remote Sensing

Cited by 30 publications

References 42 publications

Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging

Detection of Wheat Powdery Mildew by Differentiating Background Factors using Hyperspectral Imaging

Monitoring of Wheat Scab Using the Specific Spectral Index from ASD Hyperspectral Dataset

UAV Remote Sensing Image Mosaic and Its Application in Agriculture

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