Search

CIN (Cervical Intraepithelial Neoplasm) is a precancerous lesion of the cervix with a high correlation to invasive cervical cancer. Accurate detection and classification of CIN is helpful to reduce the rate of severe cervical cancer. YOLOv5-CBTR(You Only Look Once version 5 Convolutional Block Transformer) cervical lesion detection method is proposed to address the issues of low accuracy in detection and classification of cervical lesions by combining multi scale features and multiple attention mechanisms. The backbone network employs the SECSP (SENet BottleneckCSP) with SENet (Squeeze and Excitation Networks) attention mechanism for feature extraction. The Transformer encoder module is introduced to fuse and amplify multi-feature information, and multi-head attention mechanism is used to enhance the feature extraction ability of lesion regions. Convolutional attention modules are introduced into the feature fusion layer for multiscale fusion of lesion feature information. The power transformation is introduced into the calculation of the boundary regression box, which speeds up the convergence of the models loss function and realizes the detection and classification of cervical lesions. The experimental results show that the accuracy, recall rate, mAP(mean Average Precision), and F value of YOLOv5 CBTR model for the detection and classification of RGB cervical lesion images are 93.99%, 92. 91%, 92.80%, and 93.45%, respectively. The mAP and F values of the model in multispectral cervical image detection and classification are 97.68% and 95.23%, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A near-real-time recognition model was constructed to rapidly and timely identify the rice growth stages using sequential spectral images. The key stages of rice growth were identified, including tillering, jointing, heading, milking, and maturity. An optimal combination of features was also obtained. The rice experimental field was carried out in the Tianhe District, Guangzhou City, Guangdong Province, China. Unmanned aerial vehicles (UAVs) were involved in capturing spectral images of the field. The drone imaging system was equipped with one visible light and five multispectral cameras, each of which had a 2-million-pixel resolution for the high-definition captures. Detailed spectral data was obtained to precisely identify the different rice growth stages. A significant challenge was the variation in the RGB values under different lighting conditions. The reason was that the accurate segmentation of the rice canopy was hindered by using color as the primary feature. A novel spectral image segmentation was introduced to combine the Relative Vegetation Index using an iterative self-organizing data analysis. The complete area of the rice canopy was precisely extracted for the accurate identification of the growth stage. Then, the dataset was simplified to reduce the dimensions and computational complexity, in order to enhance the performance and efficiency of recognition. Among them, the expression of features was analyzed during different rice growth stages, where the reflectance data was from green, red, blue, red-edge, and near-infrared bands. Eight vegetation indices were considered as the candidate features, including the Normalized Difference Red Edge Index (NDRE), Visual Atmospherically Resistant Index (VARI), and Red Edge Chlorophyll Index (RECI). Minimum Redundancy Feature Selection was used to efficiently rank and select the most relevant and least redundant features, in order to avoid computational overload and potential impact on the model. The optimal combination of features was identified as the NDRE, VARI, and RECI, thus forming the crux of the feature engineering. Finally, this combination was integrated into the Extreme Gradient Boosting to construct an advanced recognition model of the rice growth stage. The improved model with the Extreme Gradient Boosting also exhibited exceptional performance with minimal confusion among the five growth stages. The accuracy rates of the model were 98.08%, 100.00%, 99.68%, 97.50%, and 99.29%, respectively, for the tillering, jointing, heading, milking, and maturity stages of rice. An overall recognition accuracy of 98.77% was achieved. Additionally, the improved model scored a Kappa coefficient of 0.984 and a macro-F1 score of 0.9891. The XGBoost improved the overall recognition accuracy, Kappa coefficient, and F1 score by 1.59 percentage points, 0.020, and 0.015, respectively, compared with the SVM. While the improvements were 1.23 percentage points, 0.015, and 0.011, respectively, compared with RF. The finding can provide a reliable, near-real-time, and highly accurate identification of rice growth stages. This improved model can also offer essential support for decision-making in agricultural practices, particularly in crop production and field management for the high crop yield in precision agriculture. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Institute of Food Science & Technology / Zhongguo Shipin Xuebao is the property of Journal of Chinese Institute of Food Science & Technology Periodical Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil moisture is one of the most crucial indicators to develop the intelligent irrigation in kiwifruit orchards. However, the complex kiwifruit trees with the canopy, varying coverage and substantial shading have posed some challenges on the accurate prediction of the soil moisture content. Current algorithms are still lacking on the canopy image to estimate the root soil water content (RSWC) of kiwifruit trees using canopy spectral information in the field of UAV remote sensing. In this study, a Compound Visual Convolutional Regression Network (CVCRNet) was proposed to combine two sizes of convolutional layers, in order to extract convolutional features from image data. The fully connected layers were used to reduce the dimensionality of convolutional features. Thus, multispectral images were directly analyzed for RSWC inversion. Since there was no pooling layer in the network, all data within multispectral images was fully utilized to enhance the accuracy of inversion. Multispectral images of the canopy and RSWC were collected at a depth of 40 cm during the swelling period (MaySeptember) of Xuxiang kiwifruit trees. The canopy image was processed and normalized to directly served as the input, in order to eliminate the manual feature extraction or complex structural analysis of the fruit tree canopy, as well as the correlation of vegetation indices. Deep convolutional features were extracted from the Red-Green-Near Infrared (RGN) images of kiwifruit tree canopies, in order to train the remote sensing dataset of kiwifruit orchard. The RSWC gradient maps were obtained by cubic spline interpolation. A gradient map of kiwifruit tree distribution was then generated to reflect the actual situation of water control, where the RSWC gradient maps was overlapped with the original ones. As such, the field application of the CVCRNet inversion was realized in this case. Additionally, the performance of RSWC was compared on the vegetation indices and traditional numerical models. A Multilayer Perceptron (MLP) network was introduced to establish a dual-index estimation model using Renormalized Difference Vegetation Index (RDVI) and Green Normalized Difference Vegetation Index (GNDVI). The data training showed that the epoch69 weight was selected to optimize the loss and explained variance score of the training and testing sets during CVCRNet training. The Mean Squared Error (MSE) of the training set was 1.358, with an Explained Variance Score (EVS) of 0.710, while the MSE and EVS were 0.889 and 0.737, respectively, for the testing set. The results showed that the leaves were selected in the center of the canopy in the image using CVCRNet, and then the greater weight was assigned to their reflectance information, leading to the inversion superior to traditional vegetation indices. The coefficient of determination (R² ) for the CVCRNet test set was 0.827, with a Root Mean Squared Error (RMSE) of 0.787%; R² was 0.743 and RMSE was 0.887% for all samples. The MLP test set yielded an R2 of 0.759 and an RMSE of 0.983%; R2 was 0.565, and RMSE was 2.516% for all samples. There was the significant lower CVCRNet inversion under bare ground, indicating only suitable for use during periods of high canopy coverage. The CVCRNet with images as the input was reduced the loss of multispectral image information in the complex distribution of kiwifruit orchard canopies. Canopy information extraction was enhanced to obtain the better soil moisture prediction. The soil data inversion was achieved in the complex canopy scenarios. The CNN networks can be expected for the canopy information inversion. [ABSTRACT FROM AUTHOR]

Corn is easily infected by Aspergillus ochraceus, which is severely hazardous to human health. As the traditional methods for Aspergillus ochraceus detection are time-consuming and destructive, it is necessary to develop a rapid and non-destructive method for monitoring the growth of Aspergillus ochraceus in corn during storage. In this study, multispectral imaging technology combined with chemometric methods was used to obtain the optimal model for predicting the count of Aspergillus ochraceus quantitively and classifying the infection degree qualitatively. The results showed that compared with partial least square (PLS) and least square-support vector machine (LS-SVM), back propagation neural network (BPNN) showed the best prediction performance with correlation coefficient of prediction (Ip) value of 0.9494, and the lowest root-mean-square error of calibration (RMSEC) and root-mean-square error of prediction (RMSEP) values of 2.6693 and 2.2743 CFU/g, respectively. In addition, for the classification experiment of the infective degree, BPNN was also the best prediction model with the accuracy of calibration (A c) and the accuracy of prediction ( A p) both reached 100%. The results indicated that multispectral imaging combined with chemometric methods provided a promising technique to evaluate the infection of Aspergillus ochraceus in corn. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

: Stripe rust has posed a serious threat to the wheat yield in recent years. It is crucial to breed the wheat varieties resistant to stripe rust. However, the identification of resistance is single and inefficient in traditional breeding. In this study, an efficient identification was proposed to determine the different resistance grades to the stripe rust using the time series of vegetation index response to wheat canopy. An unmanned aerial vehicle (UAV) was utilized to collect multi-temporal spectral images of the canopy in the naturally occurring breeding populations of colony wheat (600 samples in total, 516 genotypes). Six sensitive features were screened for the severity of stripe rust disease using Random Forest and ReliefF algorithms: normalized pigment chlorophyll index (NPCI), woebbecke index (WI), chlorophyll index rededge (CIrededge), (green atmospherically resistant index GARI), normalized difference vi (NDVI), and chlorophyll index green (CIgreen). These indices were verified as sensitive features. The severity of stripe rust disease incidence was dynamically characterized using the time series of these indices in the test population. The support vector machine (SVM) was used to establish a classification model for the severity grade of stripe rust disease, according to the sensitive features. There was a very small difference in the performance of the test set and the unscreened original features, indicating the effectiveness of the screened sensitive features. The time series of six sensitive traits was observed in the samples of different resistance grades. It was found that there were no significant differences in the CIgreen and CIrededge among the samples with the different resistance grades. This indicated that the saamples were not applicable to categorize the resistance grades to stripe rust. The differences exhibited by GARI, NDVI, NPCI and WI were used to classify the resistance grades to stripe rust. General machine learning cannot capture the smaller differences of feature variation in the samples with the different resistance grades. Therefore, an improved mode was proposed to extract the features from two-dimensional images that transformed vegetation index time series, in order to realize the classification of stripe rust resistance grade. Four time-series vegetation indices (NPCI, GARI, NDVI, and WI) were better distinguished the different disease resistance grades among the sensitive features, and then used to generate the Gramian Angular Summation Field (GASF) images by the Gramian Angular Field. Data augmentation was performed on the dataset to equalize the number of samples in each resistance grade. Each dataset had a total of 1 040 samples, and was then divided into four grades of stripe rust resistance, where each grade contained 260 sample images, while each dataset was divided into training, validation, and testing sets in the ratio of 6:2:2. DenseNet121 model was separately trained using each dataset, in order to classify the various stripe rust resistance. A better performance was achieved in the classification models with the GASF_NPCI and GASF_WI as the input features, compared with the GASF_GARI and GASF_NDVI. The model with the GASF_NPCI as a feature was slightly less effective in distinguishing the samples with the resistance grades R and MR, where the precision and recall were relatively low. There was no difference in the models with the GASF_WI for the precision and recall of the samples that predicted each stripe rust resistance grade. In the F1 scores of the test set, the different vegetation indices on the resistance grades of stripe rust in colony wheat were ranked in the order of NPCI, WI, GARI, NDVI. The classification model with the GASF_NPCI was the most effective in the test set, with an F1 score of up to 0.833. There was a better distinction of differences in the stripe rust resistance grades among different varieties (lines) of population wheat. The grades of wheat stripe rust resistance were fully identified using time series of spectral vegetation index. Meanwhile, the finding can also provide a strong reference for the disease resistance breeding of crops. [ABSTRACT FROM AUTHOR]

In recent years, the impact of global warming is becoming increasingly noticeable, and extreme weather is causing rapid and frequent environmental changes. Rice is Taiwan*s most important crop and one of the world's major food crops. Rice lodging majorly affects the livelihoods of farmers, and the government has been attempting to introduce technological tools to help crop damage assessment, so eligible farmers can receive compensation more efficiently. This article introduces a highly efficient assessment method through unmanned aerial vehicle based on multispectral remote sensing and AI machine learning technology on edge computing devices to assess rice lodging. The machine learning model is capable of accurately inferencing rice paddies and lodging areas with the objective and quantifiable features. This assessment method can save significant amounts of time and money for the assessment of lodging damages. This technology has the potential to be applied to large-scale rice paddy management and disaster assessments cost-effectively. [ABSTRACT FROM AUTHOR]

Monitoring the maturity of multi-species maize based on remote sensing and thus mastering the optimal harvesting time is crucial for improving its yield and quality. The traditional method to monitor the maturity progress of maize is to use field surveys, and the disappearance of the kernel "milkline" is usually taken as a sign of maturity. However, the traditional field survey method is a labor-intensive activity that is not conducive to high-throughput field monitoring. Therefore, this study aims to construct a maize maturity index (MMI) to quantify the maturity of maize and monitor it through UAV multispectral monitoring, so as to grasp the dynamics of maize maturity stage in the field. Firstly, the UAV platform was used to acquire multispectral images at five time points of the maize maturity stage, and ground-based measured data such as the percentage of milkline, kernel water content and leaf chlorophyll content were collected accordingly. Secondly, based on the weighted analysis of the measured data, the MMI was constructed. Finally, based on the MMI and the vegetation index, a model was constructed using regression models and random forests to realize the UAV multispectral monitoring of corn maturity, and the effects of different varieties on MMI were analyzed. The results showed that: 1) for different varieties of maize at maturity stage, there were differences in the change patterns of leaf chlorophyll content and kernel water content, the leaf chlorophyll content and kernel water content of Zhengdan 958 and Jingjiuqingzhu16 were always higher than that of Jiyuan 1 and Jiyuan 168, while the rate of decline of leaf chlorophyll content and milkline percentage of two varieties of maize at maturity stage was lower than that of Jiyuan 1 and Jiyuan 168. 2) The correlations between MMI and selected vegetation indices in the experiment could reach 0.01 significant level, among which the correlations with normalized difference vegetation index (NDVI) and transformed chlorophyll absorbtion ratio index (TCARI) were highest with correlation coefficients above 0.87, in addition, the wide dynamic range vegetation index (WDRVI) has the most obvious changes, and the variance fluctuates less, which is similar to MMI. 3) The study was verified based on data sets of different combinations. Among them, the random forest model has the highest estimation accuracy of MMI. The test set coefficient of determination (R² ) is 0.84, and the root mean squared error (RMSE) is 8.77%, and the normalized root mean squared error (nRMSE) is 12.05%. In the revalidation scheme 2-1, the RF model test set has high accuracy, in which R² is 0.65, RMSE is 13.02%, nRMSE is 19.17%. In addition, the random forest model has better estimated accuracy of different varieties of MMI. The Jingjiuqingzhu 16 has the best accuracy. Among them, R², RMSE, and nRMSE are 0.76, 10.67%, and 15.88%. The model accuracy proves that the drone platform can be monitored to monitor the maturity of different varieties of corn. The results of the research can provide a reference for the dynamic changes in multi-spectrum drones to monitor the dynamic changes in multi-variety of corn in farmland. [ABSTRACT FROM AUTHOR]

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objectives] In order to solve the complex problem of traditional artificial rice variety identification procedure, a visual rice variety identification model was established by using multispectral imaging technology to analyze rice leaf color. [Methods] Four machine learning algorithms, linear fitting, multi-layer perceptron, radial basis function model and random forest model, were used to model and analyze the vegetation index obtained from the feature extraction of multispectral data, which were obtained from the aerial photography of Dajiang spirit 4 multispectral unmanned aerial vehicle (UAV). The variable combinations with the best accuracy would be applied to identify rice variety with different combinations of vegetation index. [Results] The study found that the accuracy of multi-layer perceptron model in identifying rice varieties reached to 94.87%. The accuracy was 41.3%, 8.55%, and 16.24% better than that of linear fitting model, radial basis function model and random forest model, respectively. Therefore, the multilayer perceptron model with best accuracy and fitting degree was selected as the optimal model. [Conclusions] The rice variety identification model constructed in this study can accurately predict rice varieties by judging the leaf color of rice leaves, and provide reference value for the identification of rice varieties. [ABSTRACT FROM AUTHOR]

Fine-grained visual classification(FGVC) is extremely challenging due to the subtle inter-class differences and the large intra-class differences. In order to learn the embedded features of fine-grained images efficiently, this paper attempts to introduce the idea of knowledge distillation to FGVC, and proposed TRS-DeiT, which is equipped with the common advantages of CNN models and Transformer models simultaneously. Besides, a novel target regions selection module was proposed in TRS-DeiT to obtain the most discriminative regions. A contrastive loss function that measures the similarity of images was employed to distinguish the confusable classes in the task. Finally, the effectiveness of the proposed TRS-DeiT model was demonstrated through three datasets, CUB-200-2011, Stanford Cars and Stanford Dogs, which achieved the accuracy of 90.8%, 95.0% and 95.1% respectively. The experimental results shows that the proposed model outperforms the traditional models. Furthermore, the visualization results further illustrate that the attention learned by the proposed model mainly focuses on recognizing objects, thus contributes to fine-grained image classification tasks. [ABSTRACT FROM AUTHOR]

In order to achieve the real-time detection of contamination components on the surface of insulators and explore the impact of different insulators on the identification of contamination components, a hyperspectral remote sensing imaging technology based on feature band score evaluation has been proposed. The data of single fouling components for silicone rubber and ceramics has been collected by hyperspectral imaging technology, and the spectral lines are pre-processed using a model of spectral spatial adaptive total variation and a model of fouling component classification and identification based on the maximum correlation of spectral line features, which was established by a maximum correlation-minimum redundancy-back propagation neural network. The spectral spatially adaptive total variance model was able to denoise and smooth the hyperspectral curves, and obtain hyperspectral images that built a classification and recognition model. The model of maximum correlation-minimum redundancy-back propagation neural network used the fractional ranking of the spectral feature bands as the basis for filtering features, and the classification model is high accuracy, up to 92. 5% for contamination components with silicone rubber substrates and 80% for fouling components using ceramic substrates. The classification of contamination components in the hyperspectrum varies between different types of insulators as background substrates, mainly due to the degree of coverage of the fouling layer and the shape of the fouling particles; it is necessary to avoid thin areas of the fouling layer to reduce the background substrate on the detection results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problems of blurring edge contours and loss of texture details after fusion of conventional CT and MRI medical images, this paper proposed an image fusion method based on non-subsampled contourlet transform domain combined with phase consistent rolling guidance filtering (PCRGF) and improved parameter-adaptive dual channel pulse coupled neural network (PCNN) .Firstly, it used PCRGF to enhance the CT source images to improve the definition of bone contour structure. Then, it applied NSCT to decompose enhanced CT and MRI source images to obtain the high and low frequency sub-bands.Fusion of low frequency sub-band used an improved parameter-adaptive dual channel pulse coupled neural network, which significantly improved the blurring of texture details in soft tissue.It used a weighted summation modified Laplace (WSML) algorithm to fuse the high frequency sub-bands, which enhanced the fused image with more details, textures and other information in the source images. Finally, it used the inverse NSCT transformation to reconstruct the fused image.The results of five groups of comparison experiments show that the objective evaluation indexes of AG,CC,SF,MSE and CEN are improved by 13.30%,6.71%,4.40%,40.23% and 19.16%,indicate that this method performs better in enhancing the texture details, edge contours, structural similarity and image pixels of the source image. [ABSTRACT FROM AUTHOR]

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: Gaofen-7 (GF-7) is the first 1∶ 10 000 stereo mapping satellite of China that can produce high-resolution fusion products. The fusion products are fused with the 0.65 m-panchromatic images and 2.6 m-multispectral images. However, in some fusion products, halos and shifts are existed. Methods: To deal with this problem, we analyze the cross-sensor error property of push-broom optical satellite and deduce a compensation model that induces a linear term to compensate for the internal and external orientation errors, a quartic piecewise polynomial term to rectify the lens-distortion and the sub-CCD(charge coupled device) placement error, and a trigonometric series term to correct for the errors caused by platform jitter. Results: Several scenes of GF-7 (6 in Guangdong and 3 in Qinghai) were used to demonstrate the compensation model. The results show that the registration RMSE （root mean square error） decreased from 0.6 multispectral-pixels to about 0.07 multispectral-pixels by solving and applying the compensation model, and proportion of areas which have less-than 0.25 multispectral-pixels registration error increased to about 98%, which meets the requirement of producing high-quality fusion products. Conclusions: The quantitative results not only reveal the feasibility to improve the accuracy of GF-7 fusion products used by first-level data and achieve the theoretical limit accuracy, but also demonstrate the stability of the piecewise polynomial term allowing for its repeated application to further enhance compensation efficiency. [ABSTRACT FROM AUTHOR]

An accurate and universal monitoring model of nitrogen nutrition can greatly contribute to the precise management and application of regional rice. In this study, a systematic investigation was carried out to explore the impact of the images on the monitoring models. The consumer-grade multispectral images were captured by unmanned aerial vehicles (UAV) from the different ecological sites and rice varieties. A series of field experiments were conducted at two test sites: Mengzhe Town, Xishuangbanna, Yunnan Province (with trial variety Yunjing 37) and Beibei District, Chongqing City (with trial variety Jiyu 6135) in South China, with the varying nitrogen levels. Four multispectral drones (DJI Phantom) were used to capture the multispectral images of the rice canopy during tillering, jointing, and heading stages. The nitrogen content in rice plant canopies (CNC) was measured to calculate the above-ground nitrogen accumulation (PNA) using the Kjeldahl method. The nitrogen nutrition monitoring models were established using vegetation indices, partial least-squares regression (PLSR), random forest (RF), and backpropagation neural network (BPNN) for the single trial site, single variety, different trial sites, and multiple varieties of rice. The transferability was also explored in the models. The monitoring models were established for the CNC and PNA during each growth stage of rice for the single trial site and single variety with the high accuracy (normalized difference vegetation index NDVI or near-infrared normalized vegetation index NNVI, where the coefficient of determination was 0.68-0.88). However, the inaccurate model was obtained with the vegetation index during the tillering stage (NDVI, the coefficient of determination was 0.53-0.79), indicating the low transferability of all models. It was difficult for the vegetation indices to construct the monitoring models for the canopy nitrogen content throughout the entire growth period of rice. But a monitoring model was established for the above-ground nitrogen accumulation of single-variety rice in the period of the growth using the ratio vegetation index, the high accuracy and the high transferbility of the model. The monitoring models constructed by PLSR, RF, and BPNN were more accurate than those by vegetation indices. Among them, the highest accuracy was achieved in the monitoring model for the canopy nitrogen content and above-ground nitrogen accumulation in the entire growth period of multiple varieties using RF, with the coefficient of determination values were 0.84 and 0.94, respectively, and root-mean-square errors of 0.28% and 10.09 kg/hm², respectively. The finding can provide a theoretical basis and technical support for the application of consumer-grade multispectral images captured by drones in monitoring rice nitrogen nutrition during the entire growth period of different ecological sites and rice varieties. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate acquisition of canopy information was an important indicator to study the growth and yield prediction of citrus trees, but the complex tree structure had a certain impact on the accurate extraction of canopy. The rapid development of deep learning provided the possibility for accurate acquisition of citrus canopy information. Based on this, an automatic extraction method of citrus canopy based on deep learning was adopted, which combined the multispectral images collected by consumer drones with a new deep learning model U 2-Net, and constructed a dataset of citrus tree canopy by performing geometric transformation on the acquired images. The U2-Net model and the current three mainstream deep learning models ( i. e. PSPNet, U-Net and DeepLabv3+) were tested in three typical sample regions to extract the citrus canopy, and the extracted results were compared. Results showed that: in the three sample regions, the U 2-Net model had the highest extraction accuracy of citrus canopy, with the intersection over union (IoU), overall accuracy (OA) and F1-score of 91. 93%, 92.34% and 93. 92%, respectively. Compared with the other three deep learning models, the IoU, OA and F1-socre of the U 2-Net model were improved by 3.63%-8.31%, 1.17%-5. 25% and 1. 97%-4.91%, respectively. In addition, the U2-Net model had high consistency between the extraction area and the measured area of the citrus canopy, and the coefficient of determination (R2 ) of the three sample regions was higher than 0. 93, and the error rate of the U2 -Net model was lower than that of the other three deep learning models, with a root mean square error (RMSE) of 1.35 m2 and a mean relative error (MRE) of 8.15%. The results showed that the method of combining the U2 -Net model with the multi-spectral image of the drones can realize the accurate extraction of citrus canopy, and the extracted canopy contour had a good integrity, which can provide basic data and technical support for the monitoring of citrus dynamic growth changes and yield prediction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The forest area of Tibet ranks among the top in China, and the forest resources in Tibet play an important role in water conservation and ecological service. Therefore, it is of great significance to assess the assets of forest natural resources in this region. However, existing products and statistical data related to forest cover fail to meet the demands for the assessment of forest natural resource assets in this region, and it is necessary to explore a fine - scale forest classification method suitable for this region. Based on the cloud computing platform Google Earth Engine ( GEE ), this study constructed the temporal, spatial, spectral, and auxiliary feature sets of the forest coverage in Motuo County using the Landsat8 remote sensing images of 2015 and 2020, as well as field survey data, and the basic geographic data. Then, it conducted forest classification using the random forest ( RF) and classification and regression tree ( CART) algorithms. As indicated by the accuracy evaluation of the assessment results obtained using the two algorithms, the forest classification results of 2015 and 2020 obtained using the RF algorithm had relatively high accuracy, with overall classification accuracy of 0. 88 and 0. 87, respectively and Kappa coefficients of both greater than 0.8. The analyses of the areal and spatio - temporal characteristics of forest classification results show that: 1 Motuo County had a total forest area of 34 000 km ² in 2015, with a forest cover rate of up to 84. 63%, which was 2% less than that in 2020; 2 The forest resources in Motuo County are dominated by broadleaved forests, which are mainly distributed in Yarlung Zangbo Grand Canyon and low - altitude areas and accounted for 72. 27% and 75. 37% of the total forest area in 2015 and 2020, respectively. Coniferous forests accounted for 25. 96% and 23. 19% of the total forest area in 2015 and 2020, respectively and are concentrated in high - altitude areas, such as the Namcha Barwa and Gyala Peri peaks. This study determined the spatio - temporal distribution of the forests in Motuo County in 2015 and 2020 by developing a spatio - temporal -spectral classification method. It can provide a reference method for calculating specific forest cover indices SDGs and fill the gap of forest data of smaH zones. The obtained monitoring data will provide data support for the natural asset assessment and ecological function evaluation in Motuo County. [ABSTRACT FROM AUTHOR]

With the fast development and wide application of remote sensing technology, remote sensing images with higher quality are needed. However, it is difficult to directly acquire high - resolution, multispectral remote sensing images. To obtain high - quality images by integrating the information from diferent imaging sensors, pansharpening technology emerged. Pansharpening is an effective method used to obtain multispectral images with high spatial resolution. Many scholars have studied this method and obtained fruitful achievements. In recent years, deep learning theory has developed rapidly and has been widely applied in pansharpening. This study aims to systematically introduce the progrees in pansharpening and promote its development. To this end, this study first introduced the traditional, classical pansharpening methods, followed by commonly used remote sensing satellites. Then, this study elaborated on the pansharpening methods based on deep learning from the perspective of supervised learning, unsupervised learning, and semi - supervised learning. After that, it described and analyzed loos functions. To demonstrate the superiority of the pansharpening methods based on deep learning and analyze the effects of loss functions, this study conducted remote sensing image fusion experiments. Finally, this study presented the future prospects of the pansharpening methods based on deep learning. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The deep decision tree migration learning boosting method ( DtrBoost) can effectively realize the migration learning from a single source domain to a target domain under supervision, but can not realize the unsupervised migration scenario under multiple source domains. To solve this problem, this paper proposed an unsupervised transfer learning boosting method for optimizing the weight under multi-source domain distribution. The main idea was to calculate the corresponding KL value according to the distribution of different source domains and target domains, selected an appropriate number of samples from different source domains to train the classifier and pseudo label the samples from the target domain. Finally, the algorithm assigned different learning weights according to the KL distance of each different source domain, and the labeled source domain samples integrated to the pseudo labeled target domain to obtain the final result. Comparative experiments show that the proposed algorithm achieves better classification accuracy and adaptive effect on different data sets. The average classification error rate decreases by 2. 4% and more than 6% on the best marketing data set. [ABSTRACT FROM AUTHOR]

Real-time and accurate information about vegetable distribution is critical for improving the management of water/fertilizer and accurate estimation of the yield. Based on spectrum features of the segmented UAV multispectral images objects, other features including index, textural and geometric features were added to build eight classification schemes (S1~S8), and then the random forest algorithm was used to classify and analyze the classification effect. The results showed that the scheme S5 (SPEC+GLCM+INDE) performed better than the others, with overall accuracy and Kappa of 92.75% and 0.92, respectively. We discovered that adding geometric features reduced the classification accuracy, while adding index and texture features had the opposite effect. It was difficult to distinguish the Chinese cabbage and cabbage effectively only by spectrum, index and texture features, so it was necessary to introduce features such as height to improve the accuracy in the follow-up research. The feature importance ranking results showed that spectrum features, followed by the index features, had the greatest impact on classification accuracy. To summarize, the method proposed in this study, which combined object-oriented and RF algorithm, could achieve higher classification accuracy based on multi-spectral images from UAV, and the important features that affect vegetable classification could be effectively identified, providing a reference for the accurate identification of other crops. [ABSTRACT FROM AUTHOR]

In order to accurately and quickly obtain the vegetation index characteristics and vegetation coverage information of rice, the UAV was used to collect the multispectral image data of rice tillering stage, heading stage and seed setting stage, select different types of commonly used vegetation indexes, and extract and explore the vegetation index characteristics of three growth stages of rice at the scale of plot and pixel by using the ideas of sample statistics method and vegetation index intersection method, Then the threshold segmentation method was used to extract and calculate the rice vegetation information and coverage information. The results showed that in the three growth periods of rice, there were obvious phenological characteristics at the pixel and plot scales, which had obvious phenological characteristics, and there were obvious differences between weeds and trees; the extraction accuracy of vegetation coverage of multispectral vegetation index was higher than that of visible vegetation index; (normalized difference vegetation index, NDVI) had the highest accuracy in extracting vegetation coverage in three stages of rice, with extraction errors of 0.40%, 0.43% and 0.81%, R² of 0.77, 0.92 and 0.98, and (root mean square error, RMSE) of 9.09%, 2.97% and 0.38%. The extraction accuracy of (visible-band difference vegetation index, VDVI) was higher than that of (excess green-red-blue difference index, EGRBDI) and (excess green-excess red index,ExG-EXR, the extraction errors were 4.30%, 1.36% and 1.60%, respectively, R² were 0.53, 0.77 and 0.80, respectively, and RMSE were 14.62%, 3.70% and 5.50%, respectively. It could be seen that the research results can provide technical support for crop growth monitoring and vegetation coverage extraction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Irrigation & Drainage is the property of Journal of Irrigation & Drainage Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The research on the identification of dominant tree species in the coniferous forest of Shangri-La was carried out to provide a reference for subsequent forest resource management and research in the area. Based on the Google Earth Engine (GEE) platform and Sentinel-1/2 time series images of 2020, the temporal characteristics of vegetation were constructed, and a total of 43 features were combined with radar features, spectral features, texture features, and terrain features. Through different combination schemes of features, hierarchical classification and random forest classification algorithms were used to finely identify the dominant tree species of four coniferous forests of Shangri-La: Pinus densata, Pinus yunnanensis, Picea asperata and Larix gmelinii. The results showed that the classification accuracy of multi-source time series data combined with all features was the highest at three levels. The overall accuracy of forest and non-forest types in the study area was 98. 73%, and the Kappa coefficient was 0. 97, harmonic average of user accuracy and mapping accuracy F1 was 98. 71%. The overall accuracy of coniferous and broad-leaved forests was 92. 80%, the Kappa coefficient was 0. 85, F2 was 92. 58%. The overall accuracy of 4 dominant tree species was 89. 51%, the Kappa coefficient was 0. 86, F1 was 89. 36%. Different tree species had separability on different features. The combination of multiple features can improve the accuracy of tree species identification to a certain extent. Based on GEE platform and Sentinel-1/2 multi-source time series data can perform fine identification of forest dominant tree species at a spatial resolution of 10 m. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Institute of Food Science & Technology / Zhongguo Shipin Xuebao is the property of Journal of Chinese Institute of Food Science & Technology Periodical Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil salinization has posed a serious threat to the growth and yield of crops in the national food security. Among them, the Taolai River basin with the widely distributed saline-alkali land has been one of the most important planting areas in northwest China. It is a high demand for the timely acquisition of soil salinity information during the salinization control. In this study, a representative sampling area of soil salinization was taken as the Bianwan Farm in Suzhou District, Jiuquan City, Gansu Province, China. A rapid inversion model of soil salinity was proposed at the soil depths of 0-15, 15-30, and 30-50 cm under the crop cover of alfalfa and corn in the phenological period. The multi-spectral image data of the Unmanned Aerial Vehicle (UAV) was also collected at the same time. The reflectance of the spectral band was extracted in the different acquisition points of plots. The red edge band was also introduced to calculate the spectral index, in order to effectively improve the inversion accuracy. A total of 58 spectral indices were involved in the modeling. The Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was selected to screen the spectral index. Specifically, the SVM was used to sort the feature variables, and then evaluate the importance of each feature variable. The variables with low importance were removed, according to the backward iteration. As such, a better performance was achieved to effectively remove the redundant features for the high running speed of the model. A total of 36 models were constructed to evaluate the accuracy and inversion effect of the models, including Random Forest (RF), Extreme learning machine (ELM), and Back-propagation neural network (BPNN). The model input was taken as the unfiltered full and filtered new variable group. Finally, the best soil-salinity inversion model was determined for the optimal inversion depth under crop coverage. The results show that the SVM-RFE variable selection significantly improved the accuracy of each soil-salinity inversion model. A better performance was achieved in the coefficient of determination (R2), root-mean-square error (RMSE), and training speed of the improved model, compared with the model without variable screening. Overall, the inversion effect of the RF model was better than that of ELM and BPNN models. Among them, the inversion effect was one of the best indicators for real soil salt. Specifically, the RF model presented the best inversion effect in the 0-15 and 30-50 cm soil layers under crop cover, where the Rp 2 values of the validation set in the alfalfa field were 0.71 and 0.58, respectively, RMSEp values were 0.026 and 0.033, respectively; the Rp 2 values in the corn field were 0.67 and 0.64, respectively, the RMSEp values were 0.111 and 0.094, respectively. In the 15-30 cm layer, the ELM model presented the best inversion effect, where the Rp 2 values in the alfalfa and corn fields were 0.58, and 0.039, respectively; the RMSEp values were 0.68, and 0.059, respectively. In the terms of inversion depth, the inversion effects of 0-15 cm and 30-50 cm were better than that of 15-30 cm for the alfalfa-covered soil. The inversion effects of 0-15 cm and 15-30 cm were better than that of 30-50 cm for the corn-covered soil. The comprehensive analysis showed that the 0-15 cm soil layer was the best inversion depth for the soil salt content under crop cover, where the average R² of the validation set was 0.65, and the RMSE was 0.084. A strong reference was offered to manage the saline-alkali land in the arid area of northwest China. The finding can also provide a scientific basis for the rapid inversion of salt in the different soil depth layers under crop mulching. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the disadvantage that the ViT cannot change the input patch size and the input patches are all single-scale information, this paper proposed an image classification network based on Transformer called MultiFormer. MultiFormer embeds small patches with different scales of input at each stage into large patches with rich semantic information through the AWS（Attention With Scale） module; and captures local and global attention alternately through the GLA-P（Global-Local Attention With Patch） module, preserving both fine-grained and coarse-grained features during embedding. This paper designed MultiFormer-Tiny,-Small and-Base networks of three different sizes to achieve 81.1%, 82.2% and 83.2% Top-1 accuracy respectively in ImageNet image classification experiments, the latter two models improve by 3.1% and 3.4% compared to the same volume of convolutional neural networks ResNet-50 and ResNet-101; MultiFormer-Base offers a 2.1% improvement with far fewer parameters and computational effort than the ViT-Base/16 model, and without the need for extensive data pre-training. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)