Detection of rice sheath blight for in-season disease management using multispectral remote sensing

Qin, Zhihao; Zhang, Minghua

doi:10.1016/j.jag.2005.03.004

Cited by 112 publications

(64 citation statements)

References 25 publications

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“…However, because mild infected samples are easy to falsely recognize as healthy and moderate infected samples, it is still a challenge to detect mild infection in the field. Similar results were also shown in other reported studies (Qin and Zhang, 2005;. Therefore, in order to effectively direct spray control of wheat powdery mildew, data in the early-infected stage should be measured and collected.…”

Section: Impact Of Disease Severities Determination On Spray Controlsupporting

confidence: 87%

“…Space-borne and airborne remote sensing is mainly used for large-scale monitoring and forecasting of crop diseases. It is easily to assess the loss caused by disease infestation in the severely infected period but not easily to provide right spray guidance in the optimal plant protection stage (Qin and Zhang, 2005;Mewes et al, 2011); (2) In order to obtain stable spectral responses of crop diseases, studies tended to conduct in the leaf scale in laboratory condition to get rid of the complex crop growth environment in the field. There have been quite a number of studies on early diagnosis of wheat powdery mildew but they were barely verified under the field conditions (Devadas et al, 2009;Huang et al, 2013;Ashourloo et al, 2014).…”

Section: Impact Of Disease Severities Determination On Spray Controlmentioning

confidence: 99%

“…The studies of spectral analysis on rice (Qin and Zhang, 2005;Yang, 2010), wheat (Ashourloo et al, 2014a;Zhao et al, 2014), cotton (Ortiz et al, 2011;Zhang et al, 2013) and corn crop (Chen et al, 2010) showed that pigments of plant leaves have sensitive response in the visible-light range of spectrum e.g., 400-750 nm, while the leaf and canopy structures have sensitive response in the near-infrared (NIR) spectrum of 750-1000 nm. The two spectrum ranges are the major sources of information for developing VIs (Vegetation Indices).…”

Section: Introductionmentioning

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: Impact Of Disease Severities Determination On Spray Controlsupporting

confidence: 87%

Section: Impact Of Disease Severities Determination On Spray Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhang¹,

Lin²,

Huang³

et al. 2016

IJAB

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“…Different indices have been used to detect ganoderma basal stem rot disease in oil palms (Shafri and Hamdan, 2009), rice sheath blight disease (Qin and Zhang, 2005) and yellow rust infection in wheat (Huang et al, 2007). Normalized difference vegetation index (NDVI) (Rouse et al, 1973) is probably one of the most popular vegetative indices and has been also employed in this study to detect vegetation from background.…”

Section: Introductionmentioning

confidence: 99%

Band Selection Using Forward Feature Selection Algorithm for Citrus Huanglongbing Disease Detection

Katti

Lee

Ehsani

et al. 2015

Journal of Biosystems Engineering

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Purpose: This study investigated different band selection methods to classify spectrally similar data -obtained from aerial images of healthy citrus canopies and citrus greening disease (Huanglongbing or HLB) infected canopies -using small differences without unmixing endmember components and therefore without the need for an endmember library. However, large number of hyperspectral bands has high redundancy which had to be reduced through band selection. The objective, therefore, was to first select the best set of bands and then detect citrus Huanglongbing infected canopies using these bands in aerial hyperspectral images. Methods: The forward feature selection algorithm (FFSA) was chosen for band selection. The selected bands were used for identifying HLB infected pixels using various classifiers such as K nearest neighbor (KNN), support vector machine (SVM), naïve Bayesian classifier (NBC), and generalized local discriminant bases (LDB). All bands were also utilized to compare results. Results: It was determined that a few well-chosen bands yielded much better results than when all bands were chosen, and brought the classification results on par with standard hyperspectral classification techniques such as spectral angle mapper (SAM) and mixture tuned matched filtering (MTMF). Median detection accuracies ranged from 66-80%, which showed great potential toward rapid detection of the disease. Conclusions: Among the methods investigated, a support vector machine classifier combined with the forward feature selection algorithm yielded the best results.

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“…Furthermore, such destruction affects the absorption of light energy, and thus alters the reflectance spectra at different damage levels [6]. In previous studies, multi-spectral remotely sensed data were usually utilized to identify and monitor the crop stresses in several bands at a large scale in the visible and nearinfrared regions [7][8][9]. However, the crude spectral resolution of the reflected and emitted energy from the earth is the primary limiting factor in differentiating subtle differences of plant stresses [10].…”

Section: Introductionmentioning

confidence: 99%

Identifying Leaf-Scale Wheat Aphids Using the Near-Ground Hyperspectral Pushbroom Imaging Spectrometer

Zhao

Zhang

Luo

et al. 2012

Computer and Computing Technologies in Agriculture V

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Abstract. This study is to identify leaf-scale wheat aphids using the nearground hyperspectral Pushbroom Imaging Spectrometer (PIS). Firstly, the spectral characteristics between normal and aphid-infested wheat leaves were compared in spectral reflectance. Concerning the serious aphid damage level, it is obvious that its spectral curve is badly flattened such as green peak (centered around 550 nm), red valley (centered around 680 nm), due to the influence of aphid. Specifically, in the visible spectrum (500-701 nm), the maximum delta (the maximum value minus the minimum value) is 3.3 and it is 7.5 in the nearinfrared spectrum (701-900 nm). Then, the spectral difference and change rate were further analyzed. It seems that both curves show the mirror symmetry and their maximum values are 55.8% and 17.4%, respectively. For the difference curve, the value is negative in the visible spectrum (400-700 nm), which shows that the reflectance of normal wheat leaf is less than that of the serious level. Conversely, it is greater in the near-infrared spectrum (700-900 nm). Finally, based on the high spatial resolution PIS image, ENvironment for Visualizing Images (ENVI-EX) was utilized to extract aphids and the overall accuracy reaches 97%. The result indicates that the PIS is sufficient to identify the wheat aphids and this study can lay a foundation for further applications in precision agriculture using such a hyperspectral imaging system.

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Detection of rice sheath blight for in-season disease management using multispectral remote sensing

Cited by 112 publications

References 25 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

Band Selection Using Forward Feature Selection Algorithm for Citrus Huanglongbing Disease Detection

Identifying Leaf-Scale Wheat Aphids Using the Near-Ground Hyperspectral Pushbroom Imaging Spectrometer

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