Hyperspectral identification of cotton verticillium disease severity

Jin, Ning; Huang, Wenjiang; Ren, Yu; Luo, Jiancheng; Wu, Ying; Jing, Yuanshu; Wang, Dayong

doi:10.1016/j.ijleo.2012.07.026

Cited by 13 publications

(13 citation statements)

References 15 publications

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“…Zhiyong ZOU 1 , Jie CHEN 1 , Man ZHOU 2 , Zhitang WANG 3 , Ke LIU 4 , Yongpeng ZHAO 1 , Yuchao WANG 1 , Weijia WU 1 , Lijia XU 1 * (Jin et al, 2013). Mesa & Chiang (2021) used hyperspectral imaging technology combined with RGB to classify bananas (Mesa & Chiang, 2021).…”

Section: Identification Of Peanut Storage Period Based On Hyperspectr...mentioning

confidence: 99%

“…Sun et al (2020) detected the fat content of peanut kernels based on chemometrics and hyperspectral imaging technology, among which Baseline-SPA-MLR achieved high accuracy (Sun et al, 2020). Jin et al (2013) used hyperspectral to identify the severity of cotton verticillium wilt, and established a back propagation (BP) neural network, genetic back propagation (GA-BP) neural network and support vector machine (SVM) to establish four Recognition models…”

Section: Introductionmentioning

confidence: 99%

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Identification of peanut storage period based on hyperspectral imaging technology

et al. 2022

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Peanut storage time affected the quality of peanut seed sowing and germination and also affected the taste of edible peanuts. With the increase of peanut storage time, the total amount of water and amino acids decreased, and peanuts appeared moldy. The artificial judgment of peanut storage time mostly relied on visual classification to evaluate the color, which leads to large differences in color classifications between observers. This research was conducted to determine the fresh state of peanuts during storage based on the hyperspectral imaging (HSI) technology, and to identify the storage time of peanuts through hyperspectral images (387~1035 nm). Three models, two preprocessing methods, and two feature band extraction methods were combined. The experimental results shows that the DT-MF-Catboost model was the best method to detect the storage time of peanuts, and its accuracy of identifying the storage time of peanuts was 97.53%. Studies have shown that HSI has great potential in classifying the freshness and identification of peanuts, and provides a basis for non-destructive testing classification as well as grading of peanuts during storage.

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Section: Identification Of Peanut Storage Period Based On Hyperspectr...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of peanut storage period based on hyperspectral imaging technology

et al. 2022

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“…Results showed that the overall accuracies of the SVM classifications with principle components derived from the raw, first, and second order reflectance spectra for the testing dataset were 96.55%, 99.14%, and 96.55%, and the Kappa coefficients were 94.81, 98.71, and 94.82, respectively. Jin et al (2013) investigated the application of hyperspectral remote sensing in identifying cotton verticillium disease severity. A wavelet transform was employed to extract the principal information and reduce the dimensions of the hyperspectral reflectance data.…”

Section: Potential Classification Techniques: Machine Learning Algorimentioning

confidence: 99%

Remote sensing bio-control damage on aquatic invasive alien plant species

Agjee¹,

Mutanga²,

Ismail³

2015

SA J of Geomatics

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“…Qiao et al [16] built a take-all disease-grade prediction model of wheat based on the support vector machine (SVM) and found that the best training effect was achieved when choosing the 700-900 nm waveband. Jin et al [17] found that the recognition model of the verticillium wilt severity in cotton based on the combination of wavelet transform and SVM algorithm was the best. Huang et al [18] proposed the wheat scab index (WSI) by choosing the sensitive wavebands 450-488 nm and 500-540 nm, based on which a reversion model of wheat scab severity was built up.…”

Section: Introductionmentioning

confidence: 99%

Assessing Waterlogging Stress Level of Winter Wheat from Hyperspectral Imagery Based on Harmonic Analysis

et al. 2021

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Frequent waterlogging disasters can have serious effects on regional ecology, food safety, and socioeconomic sustainable development. Early monitoring of waterlogging stress levels is vital for accurate production input management and reduction of crop production-related risks. In this study, a pot experiment on winter wheat was designed using three varieties and seven gradients of waterlogging stress. Hyperspectral imagery of the winter wheat canopy in the jointing stage, heading stage, flowering stage, filling stage, and maturation stage were measured and then classified. Wavebands of imaging data were screened. Waterlogging stress level was assessed by a combined harmonic analysis method, and application of this method at field scale was discussed preliminarily. Results show that compared to the k-nearest neighbor and support vector machine algorithms, the random forest algorithm is the best batch classification method for hyperspectral imagery of potted winter wheat. It can recognize waterlogging stress well in the wavebands of red absorption valley (RW: 640–680 nm), red-edge (RE: 670–737 nm), and near-infrared (NIR: 700–900 nm). In the RW region, amplitudes of the first three harmonic sub-signals (c1, c2, and c3) can be used as indexes to recognize the waterlogging stress level that each winter wheat variety undertakes. The third harmonic sub-signal amplitude c3 of the RE region is also suitable for judging stress levels of JM31 (one of the three varieties which is highly sensitive to water content). This study has important theoretical significance and practical application values related to the accurate control of waterlogging stress, and functions as a new method to monitor other types of environmental stress levels such as drought stress, freezing stress, and high-temperature stress levels.

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Hyperspectral identification of cotton verticillium disease severity

Cited by 13 publications

References 15 publications

Identification of peanut storage period based on hyperspectral imaging technology

Identification of peanut storage period based on hyperspectral imaging technology

Remote sensing bio-control damage on aquatic invasive alien plant species

Assessing Waterlogging Stress Level of Winter Wheat from Hyperspectral Imagery Based on Harmonic Analysis

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