Diagnosis of winter-wheat water stress based on UAV-borne multispectral image texture and vegetation indices

Zhou, Yongcai; Lao, Congcong; Yang, Yalong; Zhang, Zhitao; Chen, Haiying; Chen, Yinwen; Chen, Junying; Ning, Jifeng; Yang, Ning

doi:10.1016/j.agwat.2021.107076

Cited by 56 publications

(27 citation statements)

References 45 publications

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“…In previous studies, Zhang et al (2021) used texture features, color and vegetation index to estimate wheat growth parameters, and also used texture features to compensate for index saturation and effectively improve the accuracy of the model. Zhou et al (2021) used drones to diagnose water stress in winter wheat. Zhang et al (2022) also introduced the combination of texture features and vegetation index to improve the model accuracy in the study of maize leaf area index estimation by UAV.…”

Section: Discussionmentioning

confidence: 99%

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Zhang

et al. 2022

Front. Plant Sci.

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Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R2 was 0.9109, and RMSE was 0.91277 t.ha−1. rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.

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Section: Discussionmentioning

confidence: 99%

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Zhang

et al. 2022

Front. Plant Sci.

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“…Many previous studies have shown that crop yield [ 18 , 19 ], nitrogen (N) status [ 20 , 21 , 22 ], protein content [ 23 , 24 ] and water stress [ 25 , 26 ] can be predicted by drone-based multispectral and RGB imagery. When establishing models for different crops, various spectral features including spectral reflectance, existed vegetation indices (VIs), and newly proposed VIs can be used as input variables.…”

Section: Introductionmentioning

confidence: 99%

“…Osco et al used NDVI, NDRE, the green normalized difference vegetation (GNDVI), and the soil-adjusted vegetation index (SAVI) to predict maize leaf nitrogen concentration (LNC) [ 29 ]. For water status prediction, SAVI [ 25 ], the normalized green-red difference index (NGRDI) [ 26 ], NDVI [ 30 ], NDRE [ 31 ] and so on, were reported in different studies for different crops. One of the main reasons why different spectral features are used in different crops is that the physiological characteristics and canopy distribution characteristics of different crops are different.…”

Section: Introductionmentioning

confidence: 99%

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Nong

et al. 2022

Sensors

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Sugarcane is the main industrial crop for sugar production, and its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping, since it can rapidly predict crop vigor at field scale. This study focused on the potential of drone multispectral images in predicting canopy nitrogen concentration (CNC) and irrigation levels for sugarcane. An experiment was carried out in a sugarcane field with three irrigation levels and five fertilizer levels. Multispectral images at an altitude of 40 m were acquired during the elongating stage. Partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models based on various combinations of band reflectance and vegetation indices. The simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and normalized green-blue difference index (NGBDI) were selected as model inputs due to their higher grey relational degree with the CNC and lower correlation between one another. The PLS model based on the five-band reflectance and the three vegetation indices achieved the best accuracy (Rv = 0.79, RMSEv = 0.11). Support vector machine (SVM) and BPNN were then used to classify the irrigation levels based on five spectral features which had high correlations with irrigation levels. SVM reached a higher accuracy of 80.6%. The results of this study demonstrated that high resolution multispectral images could provide effective information for CNC prediction and water irrigation level recognition for sugarcane crop.

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“…ELM is a feedforward neural network learning method, which artificially gives a hidden layer node weight without the need for updating. This method is suitable for supervised and unsupervised learning to analyze the effect of different PCs on model performance [ 24 ]. The prediction performance is shown in Figure 5 a,k.…”

Section: Results and Analysesmentioning

confidence: 99%

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

Dong

Yang

Liu

et al. 2021

Sensors

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Catechin is a major reactive substance involved in black tea fermentation. It has a determinant effect on the final quality and taste of made teas. In this study, we applied hyperspectral technology with the chemometrics method and used different pretreatment and variable filtering algorithms to reduce noise interference. After reduction of the spectral data dimensions by principal component analysis (PCA), an optimal prediction model for catechin content was constructed, followed by visual analysis of catechin content when fermenting leaves for different periods of time. The results showed that zero mean normalization (Z-score), multiplicative scatter correction (MSC), and standard normal variate (SNV) can effectively improve model accuracy; while the shuffled frog leaping algorithm (SFLA), the variable combination population analysis genetic algorithm (VCPA-GA), and variable combination population analysis iteratively retaining informative variables (VCPA-IRIV) can significantly reduce spectral data and enhance the calculation speed of the model. We found that nonlinear models performed better than linear ones. The prediction accuracy for the total amount of catechins and for epicatechin gallate (ECG) of the extreme learning machine (ELM), based on optimal variables, reached 0.989 and 0.994, respectively, and the prediction accuracy for EGC, C, EC, and EGCG of the content support vector regression (SVR) models reached 0.972, 0.993, 0.990, and 0.994, respectively. The optimal model offers accurate prediction, and visual analysis can determine the distribution of the catechin content when fermenting leaves for different fermentation periods. The findings provide significant reference material for intelligent digital assessment of black tea during processing.

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Diagnosis of winter-wheat water stress based on UAV-borne multispectral image texture and vegetation indices

Cited by 56 publications

References 45 publications

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

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