New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases

Huang, Wenjiang; Qingsong, Guan; Luo, Jiancheng; Zhang, Jingcheng; Zhao, Jing; Liang, Dong; Huang, Liusheng; Zhang, Dongyan

doi:10.1109/jstars.2013.2294961

Cited by 173 publications

(79 citation statements)

References 37 publications

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“…The evolutionary algorithm is used [14] to generate solutions for optimization problems. A method, New Spectral Indices (NSIs) is used in [15] for identifying different varieties of diseases that occurs on the crops. The NSIs are computed by using weighted algorithm and normalized wavelength.…”

Section: I4 Downy Mildewmentioning

confidence: 99%

Disease Identification in Spinach Leaves

S¹,

CHRISTOPHER²

2019

IJRTER

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Agriculture is the key development in source of Indian economy. In traditional method there is no provision in identification of disease and it can be visualized only by using naked eye. By keeping this in mind this project mainly focusing in spinach leaves disease detection.Histogram equalization technique is used for adjustment of intensity and contrast of image. Using active contour image segmentation the contour energy is minimized and specifies the curve to detect the object boundaries. The mask argument specifies the initial state of the contour. Morphological operation is performed to remove small protrusions from a segmented image. By using Histogram Oriented Gradients (HOG) feature extraction the disease affected leaves are detected. Then classification is done using ANN and identified as disease and non-disease automatically which requires less computation time and a fine result is produced.

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Section: I4 Downy Mildewmentioning

confidence: 99%

Disease Identification in Spinach Leaves

S¹,

CHRISTOPHER²

2019

IJRTER

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“…However, they still cannot reduce the impacts of soil and wheat ears on disease diagnosis with different background factors. Although some leaf-scale models of crop diseases have been built in the laboratory, the models still need to be verified in the field conditions Ashourloo et al, 2014;Huang et al, 2014).…”

Section: Calibration and Validation Of Disease Recognition Modelsmentioning

confidence: 99%

“…They selected 455 nm, 605 nm and 695 nm as the disease sensitive bands and built a new VI of wheat rust detection. Huang et al (2014) used the Relief-F algorithm to select the sensitive bands of wheat diseases. They proposed new VIs for wheat powdery mildew and wheat stripe rust.…”

Section: Introductionmentioning

confidence: 99%

“…However, the generalization and robustness of the models have to be verified in the scale of field with the data across crop varieties (Devadas et al, 2009;Ashourloo et al, 2014a;Huang et al, 2014). Up to now, there are still few literatures focused on crop disease recognition and assessment in the scale of crop canopy and field (Ashourloo et al, 2014b).…”

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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“…Huang et al [16] investigated classification of different stresses using new optimized spectral indices (NSIs) extracted from the RELIEF-F algorithm. The results showed that the NSIs were able to detect diseases and distinguish stresses with good reliability.…”

Section: Introductionmentioning

confidence: 99%

Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis

Huang

Lu²,

et al. 2018

International Journal of Agricultural and Biological Engineering

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Abstract:It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications. The winter wheat diseases, in combination with nitrogen-water stress, are therefore common causes of yield loss in winter wheat in China. Powdery mildew (Blumeria graminis) and stripe rust (Puccinia striiformis f. sp. Tritici) are two of the most prevalent winter wheat diseases in China. This study investigated the potential of continuous wavelet analysis to identify the powdery mildew, stripe rust and nitrogen-water stress using canopy hyperspectral data. The spectral normalization process was applied prior to the analysis. Independent t-tests were used to determine the sensitivity of the spectral bands and vegetation index. In order to reduce the number of wavelet regions, correlation analysis and the independent t-test were used in conjunction to select the features of greatest importance. Based on the selected spectral bands, vegetation indices and wavelet features, the discriminate models were established using Fisher's linear discrimination analysis (FLDA) and support vector machine (SVM). The results indicated that wavelet features were superior to spectral bands and vegetation indices in classifying different stresses, with overall accuracies of 0.91, 0.72, and 0.72 respectively for powdery mildew, stripe rust and nitrogen-water by using FLDA, and 0.79, 0.67 and 0.65 respectively by using SVM. FLDA was more suitable for differentiating stresses in winter wheat, with respective accuracies of 78.1%, 95.6% and 95.7% for powdery mildew, stripe rust, and nitrogen-water stress. Further analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for identification. The accuracies of high-scale and low-scale features with an overall accuracy (OA) of 0.61 and 0.73 respectively were lower than those of all wavelet features with an OA of 0.88. The detection of the severity of stripe rust using this method showed an enhanced reliability (R 2 =0.828).

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New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases

Cited by 173 publications

References 37 publications

Disease Identification in Spinach Leaves

Disease Identification in Spinach Leaves

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

Quantitative identification of crop disease and nitrogen-water stress in winter wheat using continuous wavelet analysis

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