Development and Evaluation of a New Spectral Disease Index to Detect Wheat Fusarium Head Blight Using Hyperspectral Imaging

Zhang, Dongyan; Wang, Qian; Lin, Fenfang; Yin, Xun; Gu, Chen; Qiao, Hui

doi:10.3390/s20082260

Cited by 29 publications

(21 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This clearly proved the merit of the RF classifier in modeling high-dimensional data because it intrinsically works with a random subset of features instead of all of the features of the model at each splitting point of an individual tree in the forest, thereby averaging away the feature variance. Numerous pathological and entomological vegetation studies have reported that SVM succeeded in modeling f VIs extracted from spectral bands [ 39 , 89 , 90 ], while at the same time the modeling performance of for the RF classifier was found to be stable and superlative with transformed spectral reflectance data [ 91 , 92 , 93 ].…”

Section: Discussionmentioning

confidence: 99%

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Nguyen

Sagan

Maimaitiyiming

et al. 2021

Sensors

136

View full text Add to dashboard Cite

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

show abstract

Section: Discussionmentioning

confidence: 99%

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Nguyen

Sagan

Maimaitiyiming

et al. 2021

Sensors

136

View full text Add to dashboard Cite

show abstract

“…Huang et al obtained a detection accuracy of 75% with SVM optimized with a genetic algorithm, using correlation analysis and wavelet transform for the selection of important bands, vegetation indices and wavelet features [24]. Zhang et al developed a new Fusarium disease index (FDI) after determining the best index from the existing indices with PLS regression, reaching 89.8 accuracy in detecting F. graminearum [129,130]. Whetton et al studied FHB in the laboratory and field in the wheat cultivar Solstice, using PLSR to determine FHB from yellow rust in wheat and barley [131,132].…”

Section: Hyperspectral Remote Sensing Of Wheat Diseasesmentioning

confidence: 99%

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

Terentev

Долженко

Fedotov

et al. 2022

Sensors

129

View full text Add to dashboard Cite

The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants’ disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.

show abstract

“…Spectral differential transform and continuum removal transform can eliminate some background effects and can increase implicit information, so differential spectral features and continuum removal spectral features are widely used in detecting crop disease spectra [26,27]. In addition, eight vegetation indices were selected and applied for use in the spectral detection of crop diseases [11,25,28]: (1) photosynthesis-physiological reflectance index (PHRI); (2) pigment variation parameters-triangular vegetation index (TVI), anthocyanin reflectance index (ARI) and normalized pigment chlorophyll index (NPCI); (3) greenness-greenness index (GI); (4) biophysical parameters-narrow band normalized vegetation index (NBNDVI) and plant senescence reflectance index (PSRI); and (5) water and nitrogen content-nitrogen reflectance index (NRI). The sources of definitions, references and descriptions of these 16 traditional SFs are summarized in Table 1.…”

Section: Traditional Spectral Featuresmentioning

confidence: 99%

Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

Huang

et al. 2021

Agriculture

View full text Add to dashboard Cite

Fusarium head blight, caused by a fungus, can cause quality deterioration and severe yield loss in wheat. It produces highly toxic deoxynivalenol, which is harmful to human and animal health. In order to quickly and accurately detect the severity of fusarium head blight, a method of detecting the disease using continuous wavelet analysis and particle swarm optimization support vector machines (PSO-SVM) is proposed in this paper. First, seven wavelet features for fusarium head blight detection were extracted using continuous wavelet analysis based on the hyperspectral reflectance of wheat ears. In addition, 16 traditional spectral features were selected using correlation analysis, including two continuous removal transformed spectral features, six differential spectral features, and eight vegetation indices. Finally, wavelet features and traditional spectral features were used as input features to construct fusarium head blight detection models in combination with the PSO-SVM algorithm, and the results were compared with those obtained using random forest (RF) and a back propagation neural network (BPNN). The results show that, under the same feature variables, the PSO-SVM detection method gave an overall higher accuracy than the BPNN detection method, while the overall accuracy of the RF detection model was the lowest. The overall accuracy of the RF, BPNN and PSO-SVM detection models with wavelet features was higher by 3.7%, 2.9% and 8.3% compared to the corresponding methodological models with traditional spectral features. The detection model with wavelet features combining the PSO-SVM algorithm gave the highest overall accuracies (93.5%) and kappa coefficients (0.903) in the six monitoring models. These results suggest that the PSO-SVM algorithm combined with continuous wavelet analysis can significantly improve the accuracy of fusarium head blight detection on the wheat ears scale.

show abstract

Development and Evaluation of a New Spectral Disease Index to Detect Wheat Fusarium Head Blight Using Hyperspectral Imaging

Cited by 29 publications

References 33 publications

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

Contact Info

Product

Resources

About