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OBJECTIVES To explore the diagnostic value of time-intensity curve parameters from contrast-enhanced ultrasound (CEUS) examinations of endometrial lesions. METHODS Fifty-two patients with suspected endometrial lesions who received vaginal CEUS examinations were divided into a polyp group (n = 36), a malignant group (n = 11), and a normal control group (n = 5) based on the pathologic diagnosis. The midpoint of the myometrium at the same depth as the endometrial lesion was used as the reference point. The initial increase time, time to peak, area under the curve, and peak intensity were determined by the time-intensity curve. The relative parameters, namely, the initial increase time difference, peak time difference, area ratio, and peak intensity ratio, were also calculated and analyzed statistically. RESULTS The comparison results of the relative parameters among the groups showed that the differences in the time difference, intensity ratio, and area ratio were statistically significant. The differences in the intensity ratio and time difference between the malignant and normal groups were statistically significant (P

Monitoring the chlorophyll content in tuber formation and tuber bulking stage has great significance for the nutritional status diagnosis in the potato field. In this paper, the 314 canopy spectra ...

In order to improve the diagnosis accuracy of chlorophyll content in maize canopy, the remote sensing image of maize canopy with multiple growth stages was acquired by using an unmanned aerial vehicle (UAV) equipped with a spectral camera. The dynamic influencing factors of the canopy multispectral images of maize were removed by using different image segmentation methods. The chlorophyll content of maize in the field was diagnosed. The crop canopy spectral reflectance, coverage, and texture information are combined to discuss the different segmentation methods. A full-grown maize canopy chlorophyll content diagnostic model was created on the basis of the different segmentation methods. Results showed that different segmentation methods have variations in the extraction of maize canopy parameters. The wavelet segmentation method demonstrated better advantages than threshold and ExG index segmentation methods. This method segments the soil background, reduces the texture complexity of the image, and achieves satisfactory results. The maize canopy multispectral band reflectance and vegetation index were extracted on the basis of the different segmentation methods. A partial least square regression algorithm was used to construct a full-grown maize canopy chlorophyll content diagnostic model. The result showed that the model accuracy was low when the image background was not removed (Rc2 (the determination coefficient of calibration set) = 0.5431, RMSEF (the root mean squared error of forecast) = 4.2184, MAE (the mean absolute error) = 3.24; Rv2 (the determination coefficient of validation set) = 0.5894, RMSEP (the root mean squared error of prediction) = 4.6947, and MAE = 3.36). The diagnostic accuracy of the chlorophyll content could be improved by extracting the maize canopy through the segmentation method, which was based on the wavelet segmentation method. The maize canopy chlorophyll content diagnostic model had the highest accuracy (Rc2 = 0.6638, RMSEF = 3.6211, MAE = 2.89; Rv2 = 0.6923, RMSEP = 3.9067, and MAE = 3.19). The research can provide a feasible method for crop growth and nutrition monitoring on the basis of the UAV platform and has a guiding significance for crop cultivation management.

The content of chlorophyll, an important substance for photosynthesis in plants, is an important index used to characterize the photosynthetic rate and nutrient grade of plants. The real-time rapid acquisition of crop chlorophyll content is of great significance for guiding fine management and differentiated fertilization in the field. This study used the method of continuous wavelet transform (CWT) to process the collected visible and near-infrared spectra of a corn canopy. This task was conducted to extract the valuable information in the spectral data and improve the sensitivity of chlorophyll content assessment. First, a Savitzky–Golay filter and standard normal variable processing were applied to the spectral data to eliminate the influence of random noise and limit drift on spectral reflectance. Second, CWT was performed on the spectral reflection curve with 10 frequency scales to obtain the wavelet energy coefficient of the spectral data. The characteristic bands related to chlorophyll content in the spectral data and the wavelet energy coefficients were screened using the maximum correlation coefficient and the local correlation coefficient extrema, respectively. A partial least-square regression model was established. Results showed that the characteristic bands selected via local correlation coefficient extrema in a wavelet energy coefficient created a detection model with optimal accuracy. The determination coefficient (Rc2) of the calibration set was 0.7856, and the root-mean-square error (RMSE) of the calibration set (RMSEC) was 3.0408. The determination coefficient (Rv2) of the validation set is was 0.7364, and the RMSE of the validation set (RMSEV) was 3.3032. Continuous wavelet transform is a process of data dimension enhancement which can effectively extract the sensitive variables from spectral datasets and improve the detection accuracy of models.

The tuber development and nutrient transportation of potato crops are closely related to canopy photosynthesis dynamics. Chlorophyll fluorescence parameters of photosystem II, especially the maximum quantum yield of primary photochemistry (Fv/Fm), are intrinsic indicators for plant photosynthesis. Rapid detection of Fv/Fm of leaves by spectroscopy method instead of time-consuming pulse amplitude modulation technique could help to indicate potato development dynamics and guide field management. Accordingly, this study aims to extract fluorescence signals from hyperspectral reflectance to detect Fv/Fm. Hyperspectral imaging system and closed chlorophyll fluorescence imaging system were applied to collect the spectral data and values of Fv/Fm of 176 samples. The spectral data were decomposed by continuous wavelet transform (CWT) to obtain wavelet coefficients (WFs). Three mother wavelet functions including second derivative of Gaussian (gaus2), biorthogonal 3.3 (bior3.3) and reverse biorthogonal 3.3 (rbio3.3) were compared and the bior3.3 showed the best correlation with Fv/Fm. Two variable selection algorithms were used to select sensitive WFs of Fv/Fm including Monte Carlo uninformative variables elimination (MC-UVE) algorithm and random frog (RF) algorithm. Then the partial least squares (PLS) regression was used to establish detection models, which were labeled as bior3.3-MC-UVE-PLS and bior3.3-RF-PLS, respectively. The determination coefficients of prediction set of bior3.3-MC-UVE-PLS and bior3.3-RF-PLS were 0.8071 and 0.8218, respectively, and the root mean square errors of prediction set were 0.0181 and 0.0174, respectively. The bior3.3-RF-PLS had the best detection performance and the corresponding WFs were mainly distributed in the bands affected by fluorescence emission (650–800 nm), chlorophyll absorption and reflection. Overall, this study demonstrated the potential of CWT in fluorescence signals extraction and can serve as a guide in the quick detection of chlorophyll fluorescence parameters.

Spectroscopy is an efficient way to estimate the chlorophyll content of crop canopy in the visible and near-infrared region to indicate the photosynthesis capacity and growth status. Although hyperspectral data of crop canopy provide a lot of information, a critical issue is that the subsequent redundant and interference wavelengths could reduce the accuracy and robustness of the estimated results in the field. Therefore, a cascade method of interval-wavelength screening was proposed to reduce the data dimension and improve the crop estimation accuracy and robustness for the chlorophyll content diagnosis of maize crops. In the experiments, spectral reflectance data of maize canopy within 325–1075 nm were obtained, and chlorophyll contents were measured under the conditions of six nitrogen application rates and three growth periods following the early flaring, flaring, and tasseling stages. First, the backward interval partial least squares (BiPLS) method was used to optimize the spectrum interval. Then, competitive adaptive reweighted sampling (CARS) and genetic algorithm (GA) were used for secondary wavelength screening, and the partial least squares regression (Bi-CARS-PLSR and Bi-GA-PLSR) model was established with the chlorophyll content. Compared with the full-spectrum modeling (Full-PLSR) results and the GA and CARS screening wavelength modeling (Full-CARS-PLSR and Full-GA-PLSR) results in the full-spectrum range, the number of wavelengths in the Full-PLSR model was 751. The accuracy of the Full-PLSR model with the coefficient of determination was 0.88 for the calibration set (Rc2) and 0.66 for the verification set (Rv2). The root mean square error (RMSE) was 2.05 mg/L. The wavelength numbers of Full-CARS-PLSR and Full-GA-PLSR were 125 and 99, and their Rc2 and Rv2 were 0.88 and 0.67, respectively. The RMSE values of Full-CARS-PLSR and Full-GA-PLSR were 2.04 and 1.89 mg/L, respectively. By using the proposed cascade method, the wavelength numbers of the Bi-CARS-PLSR model and Bi-GA-PLSR model after interval optimization were 60 and 49, respectively. Their accuracy values for the calibration set (Rc2) were both 0.87, while those for the verification set (Rv2) were 0.7 and 0.78, respectively. The RMSE values of both models were 1.97 and 1.86 mg/L, respectively. The results showed that the proposed cascaded interval-wavelength screening method could eliminate redundant and collinearity variables and improve model performances. At the same time, the Bi-GA-PLSR modeling result was used for field prediction to establish a chlorophyll distribution map, which had a high consistency with the real chlorophyll distribution, and showed the potential to detect chlorophyll content of maize in the field.

To achieve a non-destructive detection of chlorophyll content in field crops based on the reflectance characteristics of chlorophyll in the visible and near-infrared spectrum (400 nm–1000 nm), a crop chlorophyll detector based on an interference filter optical sensor was designed. The hardware part of this detector mainly comprises a microcontroller unit, a sensor module, an input/output module, and a power module. The software is written in Python language and includes main functions, acquisition sub-functions, data processing sub-functions, and data storage sub-functions. Calibration and test experiments were carried out to evaluate the performance of the sensor. Results show that the sensor has a good responsivity of light intensity changes, so as to measure the reflected radiation from crops with the absorption of chlorophyll content. Field verification experiments of corn crops were also carried out, and chlorophyll content detecting models were built by using four combinations of characteristic wavelengths, including 3 peak bands, 9 bands selected via the stepwise regression analysis method, 8 bands selected via the Monte Carlo uninformed variable elimination method, and all 18 bands. Among them, the stepwise regression method obtained the best modeling results. The model showed better performance after calibration than before the calibration with RC2 of 0.72, RV2 of 0.61, RMSEc of 2.35 mg/L, and RMSEv of 2.43 mg/L. The crop chlorophyll detector based on the interference filter optical sensor was used for filed estimation of chlorophyll content which showed a potential for the analysis of crop growth differences.

Leaf chlorophyll content (LCC) is one of nutritional parameters which could be estimated by Hyperspectral image (HSI) technology by combining spatial and spectral information. The objective of this study was to propose a novel segmentation algorithm to remove the influences of the veins in maize leaves so as to improve the accuracy of LCC modeling. Firstly, an enhanced green peak feature (EGPF) was built by local extremum points at 451, 552 and 648 nm, and then the image of EGPF was automatically segmented by OTSU method to get region of interest (ROI) of all leaf regions (ROI-ALR) without background and main stem. Secondly, an enhanced red valley feature (ERVF) was proposed to enlarge the edge difference between veins and mesophylls. ROI of only mesophyll regions (ROI-OMR) were extracted by removing of leaf veins resorted to edge segmentation. For the two spectral reflectance datasets from ROI-ALR and ROI-OMR, multiplicative scattering correction (MSC) was implemented. Correlation analysis (CA) and Random-Frog (RF) coupled with partial least square regression (PLSR) were applied to select characteristic wavelengths and establish LCC estimating models. Compared ROI-ALR- CA-PLSR with ROI-OMR-CA-PLSR, Rv2 increased from 0.43 to 0.52 and RMSE decreased from 4.11 to 3.61; Compared ROI-ALR- RF -PLSR with ROI-OMR- RF –PLSR, Rv2 increased from 0.83 to 0.86 and RMSE decreased from 2.14 to 1.86. The proposed HSI segmentation method for veins removal provides an effective strategy to improve LCC diagnosis of maize leaves.

The analysis of chlorophyll concentration based on spectroscopy has great importance for monitoring the growth state and guiding the precision nitrogen management of potato crops in the field. A suitable data processing and modeling method could improve the stability and accuracy of chlorophyll analysis. To develop such a method, we collected the modelling data by conducting field experiments at the tillering, tuber-formation, tuber-bulking, and tuber-maturity stages in 2018. A chlorophyll analysis model was established using the partial least-square (PLS) algorithm based on original reflectance, standard normal variate reflectance, and wavelet features (WFs) under different decomposition scales (21–210, Scales 1–10), which were optimized by the competitive adaptive reweighted sampling (CARS) algorithm. The performances of various models were compared. The WFs under Scale 3 had the strongest correlation with chlorophyll concentration with a correlation coefficient of −0.82. In the model calibration process, the optimal model was the Scale3-CARS-PLS, which was established based on the sensitive WFs under Scale 3 selected by CARS, with the largest coefficient of determination of calibration set (Rc2) of 0.93 and the smallest Rc2−Rcv2 value of 0.14. In the model validation process, the Scale3-CARS-PLS model had the largest coefficient of determination of validation set (Rv2) of 0.85 and the smallest root–mean–square error of cross-validation (RMSEV) value of 2.77 mg/L, demonstrating good prediction capability of chlorophyll concentration. Finally, the analysis performance of the Scale3-CARS-PLS model was measured using the testing data collected in 2020; the R2 and RMSE values were 0.69 and 3.36 mg/L, showing excellent applicability. Therefore, the Scale3-CARS-PLS model could be used to analyze chlorophyll concentration. This study indicated the best decomposition scale of continuous wavelet transform and provided an important support method for chlorophyll analysis in the potato crops.

In this era of big data, it is difficult to extract the information law of agricultural products prices from the vast amount of agricultural products. In this paper, we select 6 kinds of agricultural products prices in 2014, and use the association rule algorithm to analyze the correlation between them, and then get the strong association rules among the prices of agricultural products. Empirical results show that the price of corn in 2014 had a strong correlation with the price of soybean. The price trend of these two is basically the same. In the study of corn or soybean prices, we can refer to either price chart. The experimental results demonstrate the correctness of the algorithm. The application of the algorithm in the analysis of the price of agricultural products can play a positive role in guiding the analysis and prediction of the price of agricultural products.

Background: To investigate the important impacting factors on the accuracy of local peak strain (RLS) in transthoracic echocardiography real-time tri-axial automatic functional imaging (AFI), to evaluate the clinical efficacy of AFI and to improve the accuracy of the results. Methods: From May 2016 to May 2017, 82 healthy volunteers were enrolled in the AFI examination, of which 22 were excluded and 60 were eligible. The excluded 22 patients were analyzed for exclusion reasons, and the results of the 60 eligible AFI results were studied focusing on the longitudinal left ventricular regional longitudinal peak systolic strain (RLS) in different methods of operation, to compare the results and accuracies of AFI by different influencing factors, and to find the most important ones. Results: The success rate of AFI for this group of subjects is 74%, and the exclusion reason is that the left ventricular segments cannot be fully displayed. Among eligible subjects, the main influencing factors on RLS were region of interest (ROI), aortic valve closure time adjustment and image frame rate selection. The differences of results obtained by different operations were statistically significant (P Conclusions: The success rate of AFI for this group of subjects is 74%. The RLS results were influenced by multiple factors, which can be effectively avoided.