The Leaf Area Index (LAI) strongly influences crop biomass production and yields. The variation characteristic of LAI and the development of crop growth models can provide a theoretical basis for predicting crops’ water consumption, fruit quality and yields. This paper analyzes the relationship between measurements of aboveground grape biomass and trends in LAI and dry biomass production in grapes grown in the Turpan area. The LAI changes in grapes were estimated using the modified logistic model, the modified Gaussian model, the log-normal model, the cubic polynomial model, and the Gaussian model. Universal models of LAI were established in which the applied irrigation quota was applied to calculate the maximum LAI. The relationship between the irrigation quota and biomass production, yields, and the harvest index was investigated. The developed models could accurately predict the LAI of grapevines grown in an extremely arid area. However, the Gaussian and cubic polynomial models produced less accurate results than the other models tested. The Michaelis–Menten model analyzed the relationship between biomass and LAI, providing a numerical method for predicting dynamic changes in grapevine LAI. Moreover, the crop biomass increased linearly with the irrigation quota for quotas between 6375 and 13,200 m3/hm. This made it possible to describe the grape yield and harvest index with a quadratic polynomial function, which increases during the early stages of the growing season and then decreases. The analyses of the relationship between yield and harvest index provide important theoretical insights that can be used to improve water use efficiency in grape cultivation and to identify optimal irrigation quotas.

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Mathematical Models of Leaf Area Index and Yield for Grapevines Grown in the Turpan Area, Xinjiang, China

With the development of multimedia technology, more and more applications used in digital image processing, tampering with digital images is become easier. This paper proposes a new approach for image detection based on color tampering by encoding multiple color channels features based on VLAD without embedding watermarks. We have counted multiple sets of common color channels in computer vision, choosing the most suitable combination of color channels, and then using VLAD to encode these selected features, finally to train a SVM model by encoded features, we also found that when a lot of classes image were mixed together cannot be will detected, so we adopted deep learning to train a ResNet of classification, and first to classify our dataset, in comparison with state-of-theart method, various experiments result prove that the proposed approach achieves better performance in computer generated colorized image forgery detection.

Computer Generated Colorized Image Forgery Detection using VLAD Encoding and SVM

Blind Image tamper detection algorithm based on Radon and Fourier-Mellin transform

Anthocyanins are severity indicators for apple mosaic disease and can be used to monitor tree health. However, most of the current studies have focused on healthy leaves, and few studies have estimated the anthocyanin content in diseased leaves. In this study, we obtained the hyperspectral data of apple leaves with mosaic disease, analyzed the spectral characteristics of leaves with different degrees of Mosaic disease, constructed and screened the spectral index sensitive to anthocyanin content, and improved the estimation model. To improve the conciseness of the model, we integrated Variable Importance in Projection (VIP), Partial Least Squares Regression (PLSR), and Akaike Information Criterion (AIC) to select the optimal PLSR model and its independent variables. Sparrow Search Algorithm-Random Forest (SSA-RF) was used to improve accuracy. Results showed the following: (1) anthocyanin content increased gradually with the aggravation of disease. The reflectance of the blade spectrum in the visible band increased, the red edge moved to short wave, and the phenomenon of “blue shift of spectrum” occurred. (2) The VIP-PLSR-AIC selected 17 independent variables from 21 spectral indices. (3) Variables were used to construct PLSR, Back Propagation (BP), Support Vector Machine (SVM), Random Forest (RF), and SSA-RF to estimate anthocyanin content. Results showed the estimation accuracy and stability of the SSA-RF model were better than other models. The model set determination coefficient (R2) was up to 0.955, which is 0.047 higher than that of the RF model and 0.138 higher than that of the SVM model with the lowest accuracy. The model was constructed at the leaf scale and can provide a reference for other scale studies, including a theoretical basis for large-area, high-efficiency, high-precision anthocyanin estimation and monitoring of apple mosaics using remote sensing technology.

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Estimation of Anthocyanins in Leaves of Trees with Apple Mosaic Disease Based on Hyperspectral Data

Focusing on the face stitching tampering image,a splicing image tamper detection method based on human face color temperature was proposed.Using the technology of target detection,the method in question pinpointed the faces in the image,and then estimated the color temperature of all those faces and the reference color temperature.After calculating the discrepancy between human face color temperature and reference color temperature,the method could find out the detection target with abnormal color temperature discrepancy as the tampering target,and then could locate the precise position of the tampering target.The empirical study showed that the algorithm was effective.

Splicing image tamper detection based on human face color temperature

In the era of rapid development of multi-media information, forgery detection has become an important research field of digital image security. This paper proposes a new method to detect the forged image generated by deep learning. First, the feature matrix is constructed through extracting each pixel value of channels a and b in Lab color space for the real and the forged image training set, respectively, which is used to fit the Gaussian Mixture Model (GMM) distribution. Then, the Expectation Maximization (EM) adaptation algorithm is used to re-fit the GMM for test image using the obtained GMM parameter as prior information. Finally, the similarity between two GMM is calculated for forgery detection. Experiments show that the proposed method is more accurate than the traditional SVM for forgery detection.

Colorized Image Forgery Detection based on Similarity Measurement of Gaussian Mixture Distribution

ABSTRACT The leaf area index (LAI) is an important agroecological physiological parameter affecting vegetation growth. To apply the genetic algorithms neural network model (GANNM) to the remote sensing inversion of winter wheat LAI throughout the growth cycle and based on GaoFen-3 Synthetic aperture radar (GF-3 SAR) images and GaoFen-1 Wide Field of View (GF-1 WFV) images, the Xiangfu District in the east of Kaifeng City, Henan Province, was selected as the testing region. Winter wheat LAI data from five growth stages were combined, and optical and microwave polarization decomposition vegetation index models were used. The backscattering coefficient was extracted by modified water cloud model (MWCM), and the LAI was obtained by MWCM inversion as input factors to construct GANNM to invert LAI. The root mean square error (RMSE) and determination coefficient (R 2) were used as evaluation indicators of the model. The fitting accuracy of winter wheat LAI in five growth stages by GANNM inversion was better than that of the BP neural network model; the R 2 was higher than 0.8, and RMSE was lower than 0.3, indicating that the model could accurately invert the growth status of winter wheat in five growth stages .

https://doi.org/10.1080/17538947.2022.2028913

Winter wheat leaf area index inversion by the genetic algorithms neural network model based on SAR data

Leaf area index (LAI) is an important parameter that determines the growth status of winter wheat and impacts the ecological and physical processes of plants in ecosystems. The problem of spectral saturation of winter wheat LAI at the booting stage was easily caused by the inversion of the univariate red-edge spectral vegetation index constructed by the red-edge band. In this paper, a new method that the univariate red-edge spectral vegetation index constructed in the red-edge band is used to invert the spectral saturation of the winter wheat LAI. The multivariable red-edge spectral vegetation index is used to invert the winter wheat LAI. This method can effectively delay the phenomenon of spectral saturation and improve the inversion precision. In this study, the Sentinel-2 data were used to invert the winter wheat LAI. An univariate and multivariate red-edge spectral vegetation index regression model was constructed based on the Red-edge Normalized Difference Spectral Indices 1 (NDSI1), Red-edge Normalized Difference Spectral Indices 2 (NDSI2), Red-edge Normalized Difference Spectral Indices 3 (NDSI3), Modified Chlorophyll Absorption Ratio Index (MCARI), MERIS Terrestrial Chlorophyll Index (MTCI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), and Transformed Chlorophyll Absorption in Reflectance Index/the optimized soil adjusted vegetation index (TCARI/OSAVI). Based on the correlation coefficient, the coefficient of determination (R2), the root mean square error (RMSE) and noise equivalent value (NE), the best model was selected and verified to generate an inverted map. The results showed that the multivariable red-edge spectral vegetation index of NDSI1 + NDSI2 + NDSI3 + TCARI/OSAVI + MCARI + MTCI + TCARI was the best model for inverting the winter wheat LAI. The R2, the RMSE and the NE values were all satisfied the requirements of the inversion precision (R2 = 0.8372/0.8818, RMSE = 0.2518/0.1985, NE = 5/5). In summary, this method can be used to judge the growth of winter wheat and provide an accurate basis for monitoring crop growth.

Inversion of Wheat Leaf Area Index by Multivariate Red-Edge Spectral Vegetation Index

Abstract The leaf area index (LAI) is an important agroecological physiological parameter affecting vegetation growth and is an essential indicator for estimating crop growth. To apply the hybrid machine learning model to the remote sensing inversion of winter wheat LAI at three growth stages based on the wide field of view (WFV) of the GaoFen-6 (GF-6 WFV) images, the Xiangfu District in the east of Kaifeng City, Henan Province, was selected as the testing region. The LAI values, which were simulated using the CERES-wheat model, were an input for the PROSAIL model to simulate spectral reflectance. Different machine learning regression algorithms (MLRA) were trained with simulated spectra reflectance by PROSAIL and subsequently applied to the GF-6 WFV reflectance spectra. The random forest regression (RFR) model achieved reliable LAI estimates at the three growth stages. CERES-wheat and PROSAIL preserved the most informative spectra reflectance for LAI estimation so that each RFR could achieve satisfactory estimation results. The hybrid machine learning model could accurately reverse the growth state of winter wheat in three growth stages.

Inversion of the hybrid machine learning model to estimate leaf area index of winter wheat from GaoFen-6 WFV imagery

Abstract When segmenting high-spatial-resolution remote sensing images using convolution neural networks, it is difficult to balance the number of segmented regions against the contour accuracy. This article proposes a segmentation method that combines the minimum area adaptive watershed transform based on morphological reconstruction with the modified deep edge detection network (MDEDNet). First, the minimum area adaptive watershed algorithm is adopted to reconstruct the gradient features of the image and perform watershed segmentation, giving the initial segmentation results. A ground object contour optimization method and MDEDNet are then used to further improve the accuracy of the segmentation, resulting in the final segmented image. Experiments on image sets of varying complexity indicate that the algorithm proposed in this article can achieve accurate image segmentation with good segmentation results and fast operation efficiency.

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2038694?needAccess=true

Ze Yang

Papers

Colorized Image Forgery Detection based on Similarity Measurement of Gaussian Mixture Distribution

Winter wheat leaf area index inversion by the genetic algorithms neural network model based on SAR data

Inversion of Wheat Leaf Area Index by Multivariate Red-Edge Spectral Vegetation Index

Inversion of the hybrid machine learning model to estimate leaf area index of winter wheat from GaoFen-6 WFV imagery

High-spatial-resolution remote sensing image segmentation using adaptive watershed-driven joint MDEDNet