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

Huang, Weixin; Lu, Junjing; Ye, Huichun; Kong, Weibao; Mortimer, A. Hugh; Shi, Yuejiang

doi:10.25165/j.ijabe.20181102.3467

Cited by 29 publications

(21 citation statements)

References 32 publications

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“…In quantifying crop diseases, wavelet features were demonstrated to outperform conventional spectral features [18,19]. Additionally, some studies illustrated that CWA performed well in differentiating crop stresses [20][21][22][23][24]. The above results demonstrate the superiority of CWA for crop pest and disease monitoring.…”

Section: Introductionmentioning

confidence: 95%

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Huang

Jing

et al. 2019

Sensors

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Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R2 values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears.

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Section: Introductionmentioning

confidence: 95%

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Huang

Jing

et al. 2019

Sensors

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“…Due to the overlap between chlorophyll and carotenoid absorption peaks and due to higher concentration of chlorophyll than carotenoid in most leaves, it becomes difficult to estimate carotenoid with the help of reflectance. Carotenoid concentration relative to chlorophyll can be concluded by higher value of CRI1 [22]. Photochemical Reflectance Index (PRI) reacts when changes in carotenoids take place in live foliage, especially with xanthophyll.…”

Section: Fig (3)spectral Graph Of Diseased Leavesmentioning

confidence: 99%

Spectral and Numerical Analysis of Hyper spectral Data using Vegetation Indices

Sapate*,

Deshmukh

2019

IJEAT

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Indian economy is majorly influenced by Agriculture and its allied sectors. More than 50% of the population of India is dependent on agriculture and its allied sectors for their survival. According to the Report of Indian Council of Agricultural Research, 30-35% of the crop yield gets wasted due to disease. Using modern day remote sensing techniques plant health can be monitored and it can be specified whether a plant is diseased or healthy. Hyperspectral Remote Sensing is the technique by which fine and minute information of vegetation can be obtained with the help of narrow wavebands. Data of 80 leaf samples of Tomato crop collected in spectra form and text form using ASD FieldSpec4 spectroradiometer and ViewSpec PRO. This information of plant leaves was used to identify vegetation attributes and its status. Vegetation Indices are calculated using mathematical formulae published in the previous study. Random forest classification used to discriminate among Healthy and Diseased plants. Algorithm works with an accuracy of 93.75% with misclassification rate 0.0625. Along with Green wavelength range and Red edge of the spectrum, specific disrupted behavior was observed in Shortwave Infrared Region of the spectra. The research paper focuses on Spectral and Numerical study and analysis of Tomato Leaf disease with the help of ASD FieldSpec4.

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“…In order to predict the air-fuel ratio of the HHHTS, an appropriate identification method must be proposed. At present, the popular system identification methods mainly include artificial neural network [17], [18], fuzzy logic [19], [20], genetic algorithm [21], [22], wavelet analysis [23], [24] and support vector machine (SVM) [25], [26]. Compared with other identification methods, SVM not only has the advantages of simple algorithm, good robustness and strong generalization ability, but also shows many unique advantages in solving the problem of limited sample and nonlinear identification [27], which is more suitable for the air-fuel ratio identification of the HHHTS.…”

Section: Introductionmentioning

confidence: 99%

Identification of Air-Fuel Ratio for a High-Temperature and High-Speed Heat-Airflow Test System Based on Support Vector Machine

2020

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Air-fuel ratio is an important parameter in high-temperature and high-speed heat-airflow test system. If air-fuel ratio of the system is too low, the fuel cannot be fully burned, which will not only reduce the control performance of the gas temperature, but also increase the pollutant emissions of the combustor. In order to solve this problem, it is necessary to identify the air-fuel ratio of the system, get the prediction model of the air-fuel ratio, and adjust the fuel input according to the prediction value of the air-fuel ratio. In order to realize the accurate identification of the air-fuel ratio of the system, this paper briefly analyses the mathematical model of the air-fuel ratio in high-temperature and high-speed heat-airflow test system, and proposes an identification method of the air-fuel ratio based on support vector machine. On the basis of the experimental data, the air-fuel ratio of the system is identified by using different kernels, i.e. firstly, the experimental scheme is designed, and the fuel mass flow rate, air mass flow rate, gas temperature and actual air-fuel ratio of the system are collected under different experimental conditions; then, the collected data are divided into training datasets and test datasets, and the training datasets are trained by support vector machine to obtain identification model of the air-fuel ratio; finally, the identification model is validated with test datasets under different conditions, and the accuracy of the model is obtained. The identification results show that the support vector machine has good identification performance and can accurately approximate the actual dynamic process of the air-fuel ratio. The average absolute error of the identification model is less than 0.05, and the average relative error is less than 0.5% when the test datasets are smaller than the training datasets.

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

Cited by 29 publications

References 32 publications

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Spectral and Numerical Analysis of Hyper spectral Data using Vegetation Indices

Identification of Air-Fuel Ratio for a High-Temperature and High-Speed Heat-Airflow Test System Based on Support Vector Machine

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