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As an important economic tree species in China, tea trees play a critical role in promoting economic development. However, the expanding area of tea plantations in China over the recent years has reduced the biodiversity of the ecosystem and accelerated ecological problems. Identifying the spatial distribution of the tea plantation accurately is important for the monitoring of the tea plantation as well as the development of the tea industry. In this study, Wangzhai Town, located in the central of Wuyi County in Zhejiang Province, was chosen as the study area, and Gaofen-1 (GF-1) data on December 28, 2017, and all available Sentinel-2 data in 2017 were downloaded to study the tea plantations extraction method. The spectral and texture features of tea plantations are similar with crops on the high spatial resolution remote sensing image which results in the mixture of the tea plantations and crops. The Normalized Difference Vegetation Index (NDVI) time series data can reflect the phenological changes of the tea trees and crops, which can be used to improve the separability of tea plantations and crops. In this study, a phenology-based approach was applied to the NDVI time series to distinguish tea plantations from crops by comparing the similarity of the seasonal changes of NDVI time series. Due to the influence of cloud and fog on the satellite image, the complete Sentinel-2 time series data is difficult to be obtained. Therefore, the high temporal resolution Moderate-resolution Imaging Spectroradiometer (MODIS) reflection product (MCD43A4) in 2017 was used to reconstruct the complete Sentinel-2 NDVI time series data (5 m spatial resolution, 5 d temporal resolution) based on Bayesian Maximum Entropy (BME) spatial-temporal fusion algorithm. Then, the Dynamic Time Wraping (DTW) distance was calculated based on the DTW algorithm to characterize the similarity of NDVI time series between the tea plantation samples and other pixels to be classified. Combining the advantages of GF-1 in spatial details and the high temporal resolution of reconstructed Sentinel-2 NDVI time series in capturing the difference of growth process of tea trees, the tea plantation was identified based on random forest algorithm. Two identification schemes: 1) spectral and texture features based on GF-1; 2) spectral, texture features based on GF-1 and DTW distance were set up to investigate the impact of NDVI time series on the tea plantation identification. The results showed that: 1) the reconstructed NDVI time series data derived from MODIS and Sentinel-2 based on spatio-temporal fusion algorithm is able to capture the seasonal dynamics of different objects, and the DTW distance can capture the difference of NDVI time series data between crops and tea plantations and can be used for tea plantations extraction; 2) the accuracy、 error rate、precision、recall and F1score were 96.91%、3.09%、89.00%、83.09% and 0.86 respectively combining texture and spectral information of GF-1 and Sentinel-2 NDVI time series information, which were 94.72%、5.28%、73.09%、84.61% and 0.78 respectively without Sentinel-2 NDVI time series information. Overall, the results of combining GF-1 and Sentinel-2 time series data performed better than that only based on the spectral and texture features of GF-1. These results confirmed that combining high spatial resolution with high temporal resolution time series remote sensing data is an effective method to improve the identification accuracy of tea plantations. [ABSTRACT FROM AUTHOR]

Objective To summarize the dynamic changes of cerebrospinal fluid (CSF) cytology in drug resistant tuberculous meningitis (TBM), and to explore its value in drug resistance and efficacy evaluation of TBM. Methods Seventy-nine patients with TBM who were diagnosed and treated in Jiangxi Chest Hospital from January 2013 to December 2020 were included in this study. H(S)REZ(V) scheme [isoniazid (or streptomycin), rifampicin, ethambutol, plus pyrazinamide (or levofloxacin)] regimen was adopted before the return of drug sensitivity test results. After the return of results, individualized anti - tuberculosis regimen was formulated according to drug resistance. Results According to the drug sensitivity test results of CSF, 37 cases were drug resistant TBM, of which 29 cases (78.38%) were multidrug resistant. The drug resistance spectrum was isoniazid + rifampicin + streptomycin [35.14% (13/37)], isoniazid + rifampicin + streptomycin + fluoroquinolone [18.92% (7/37)], isoniazid + rifampicin + ethambutol + streptomycin [16.22% (6/37)], isoniazid and rifampicin [10.81% each, (4/37)], Isoniazid + rifampicin, isoniazid + rifampicin + ethambutol + streptomycin + fluoroquinolone, isoniazid + rifampicin + ethambutol + streptomycin + amikacin/capreomycin [2.70% each, (1/37)]. After 2 and 4 weeks of antituberculosis treatment, the number of nucleated cells in CSF of non drug resistant group (t = 5.050, P = 0.000; t = 11.100, P = 0.000) and neutrophil ratio (t = 15.268, P = 0.000; t = 17.048, P = 0.000) were lower than drug resistant group. CSF cytology showed that the drug resistant group showed mixed cell inflammatory reaction lasting more than 4 weeks, and the non drug resistant group transformed into significant lymphocyte reaction. After 6 months of follow-up, the total effective rate of drug resistant group was lower than that of non drug resistant group [45.71% (16/35) vs. 90% (36/40); χ² = 17.218, P = 0.000]. Conclusions The cytological characteristics of CSF in patients with TBM are mainly characterized by mixed cell inflammatory reaction in the early stage, the delayed mixed cell inflammatory reaction lasting more than 4 weeks during treatment is the characteristic of drug resistant TBM, which may be the basis for indicating that the treatment plan is invalid. When limited by the time limit and conditions of drug sensitivity test, monitoring the dynamic changes of CSF cytology may become an auxiliary judgment method for drug resistant TBM, which can provide reference for clinical efficacy evaluation and antituberculosis treatment plan adjustment. [ABSTRACT FROM AUTHOR]

Precise irrigation has been a highly urgent need to realize a quick, accurate, and non-destructive acquisition for the distribution of moisture content in reclaimed soil, particularly in coal mining subsidence areas. Consequently, traditional measurement can be replaced with Ground-Penetrating Radar (GPR), a new type of near-earth microwave remote sensing with large-scale continuous detection, fast, non-destructive, and low-cost. GPR has also been widely used in the detection of water content in soil. However, GPR is rarely used in the detection of water content in reclaimed soil, due to the complex composition of reclaimed soil and the fast attenuation of electromagnetic waves in cohesive soil. Fortunately, spectrum analysis can convert the radar data (signal change) from the time domain to the frequency domain. Energy distribution of signal frequency can be used to alleviate a large wave speed error under different media. In this study, the soil of a subsided reclamation area in Huaibei City, Anhui Province of China was taken as the research object. A control group was set up in the surrounding normal areas. The Chirp-Z-transform (CZT) was used to find the characteristic frequency. Rayleigh scattering was selected to analyze the characteristic spectrum response of radar signals under volumetric water content in soil. Various regressions were used to establish inversion models of water cut. The results showed that the frequency of peak (FP) using the Rayleigh scattering had an excellent correlation with the volumetric water content of reclaimed soil, where the correlation coefficient reached 0.90, indicating that the radar signal FP was feasible to invert the soil volumetric water content of the reclaimed area. Moreover, the CZT effectively identified the characteristic frequency in a higher resolution without changing the original frequency spectrum. The correlation coefficient between FP and soil moisture content was improved from 0.77 to 0.93, compared with the fast Fourier transform (FFT). In addition, the spectrum of radar signal in the reclaimed area demonstrated a greater drift than that in the non-reclaimed area under the same change of water content. The bulk density was also one of the factors that affect the radar signal. The highest accuracy was achieved in the exponential regression, where the verification precisions were R2 =0.84, and RMSE=1.97%, showing that the frequency spectrum analysis was an effective way to invert the moisture content of cultivated soil. When large construction equipment was used to compact the reconstructed soil, there will be a larger influence on the distribution of soil moisture content and the fertility of reclaimed soil. The findings can provide an insightful theoretical basis and technical support for rapidly monitoring the soil quality and precision irrigation in reclaimed areas. [ABSTRACT FROM AUTHOR]

A hyperspectral reflectance can be expected as an alternative method to quickly predict the concentration of heavy metals in soil at present. However, excessive band counting of hyperspectral data can lead to information redundancy during the quantitative inversion of surface parameters, and thereby to make it difficult to extract valid data from the spectrum. In this study, two aspects were addressed to solve spectral redundancy during the inversion of Pb content in soil: One is to extract the characteristics of iron oxides from the soil spectrum, according to the adsorption mechanism of Pb element in soil, in order to reduce data redundancy, while enhance the mechanism of inversion. Another is to propose an Improved Genetic Algorithm (IGA), in order to deal with the premature convergence of traditional Genetic Algorithm (GA), further to improve the extracting ability of algorithm for the required spectrum. Taking the Xiong’an New Area near Beijing, a national new-district in China, as the research region, the field experiment was carried out to quickly collect the Pb content of soil in the farmland, resulting from the rapid construction and developing facilities in recent years. 70 soil samples from the Xiong’an farmland were selected to establish the inversion model of Pb content. SVC HR1024I type ground object spectrometer was used to measure the raw hyperspectral reflectance of soil samples according to the national standard procedure in field farmland. After that, the collected soil samples were transferred to the laboratory for chemical analysis, particularly for the Pb content. Before modeling, the methods, including Savitzky-Golay filter, envelope removal and first-order difference, were used to pretreat with segmented the curves of soil reflectance. Partial Least Squares Regression (PLSR) was selected to serve as inversion model, in order to well deal with the strong correlation between hyperspectral bands. The models were also constructed in terms of full spectrum and characteristic bands of iron oxides, to verify the effectiveness of extracting method from characteristic bands of iron oxides during the inversion process. In full spectrum, the average values of R2, RPD, RMSE in the IGA-PLSR models were 0.440, 1.366, and 4.012 mg/kg, respectively. In the characteristic bands of iron oxides, the average values of R2, RPD, RMSE in the IGA-PLSR models were 0.822, 2.377, and 2.221 mg/kg, respectively. It infers that the models using the characteristic bands of iron oxides were much better than those using the full spectrum, indicating that the extraction for the characteristic bands of iron oxides can be contributed to reduce the redundancy of spectral information, and thereby to significantly improve the accuracy of the inversion model. Consequently, a final inversion model was selected from the optimal model for the improvement of inversion algorithm, based on the utilization of the characteristic bands of iron oxides to construct the GA-PLSR and IGA-PLSR models with different iterations. In the GA-PLSR models, the average values of R2, RPD, RMSE in the optimal model were 0.788, 2.140, and 2.447 mg/kg, respectively, whereas, the IGA-PLSR model achieved the highest accuracy, where the average values of R², RPD, RMSE were 0.837, 2.403, and 2.063 mg/kg, respectively. Therefore, the optimal model of IGA-PLSR was chosen to estimate the Pb content in soil for the Xiong’an farmland area. The IGA-PLSR method has demonstrated better performance in the inversion of Pb content in soil, indicating that the proposed method was valid to improve genetic algorithm. At last, a visualized profile can be conducted for the changes of RMSE during the 1000 iterations of two models. The visualization revealed that the RMSE in the GA-PLSR model decreased monotonously during the operation process, whereas, the RMSE in the IGA-PLSR model fluctuated greatly at the beginning of the iteration, showing a certain probability to jump out from the local solution area to a better solution area at the later stage, while meaning that the problem of “premature convergence” was solved properly. This study revealed that two aspects, including the extraction of iron oxides spectral features associated with Pb absorption, and the improved algorithm of bands selection, both can be used to enhance the accuracy of Pb content estimation in soil. The proposed algorithm can provide a sound reference to real-time monitor the variation of heavy metal content in soil for Xiong’an area, China. [ABSTRACT FROM AUTHOR]

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Tomato has become the most cultivated and consumed vegetable crop in the world, and China has become one of the largest producers and consumers of tomatoes in the world. The pectin content in cell wall of tomato during ripening is closely related to fruit hardness, which is one of the important indicators to determine the maturity and reflect the quality of tomato. The requirement of tomato maturity classification and evaluation promotes the development of non-destructive, fast and accurate detection methods of tomato hardness.Hyperspectral imaging integrates spectroscopy and imaging technology in an analysis system, which transfers tomato maturity assessment from subjective, manual classification and measurement methods. Hyperspectral imaging has been widely used in the rapid acquisition of information to classify, detect or identify the quality of various fruits. A novel method for tomato hardness detection based on hyperspectral imaging and modified interval Random Frog was proposed in this paper. Firstly, hyperspectral images of 120 tomato samples in different mature periods were captured by hyperspectral imaging system covering near-infrared region (865.11nm-1 711.71nm). And the hardness data of tomato was obtained by texture analyzer. Secondly, the spectral data were pretreated by multiplicative scatter correction (MSC) and normalized preprocessing to eliminate noise and improve signal-to-noise ratio. The validity of the characteristic wavelength plays a crucial role in the prediction performance of the model. Therefore, we need an effective method to extract the effective wavelength to improve the accuracy of the model. Interval random frog (iRF) algorithm considers all possible spectral wavelengths and ranks all the wavelengths based on selected probability. But one of the disadvantages of this method is large number of iterations and slow convergence. In view of above disadvantages, the traditional iRF algorithm was optimized in terms of constructing initial variable subset method. A modified interval Random Frog (miRF)was proposed to extract the characteristic wavelength effectively. Finally, a prediction model was developed based on partial least squares regression (PLSR) method to detect tomato hardness. The results indicated that the convergence efficiency and accuracy of miRF has a significantly improvement compared with the iRF method. The iRF has selected 100 feature bands, accounting for 40.65% of the full band, and its runtime was 32.1min. miRF has selected 47 feature bands, accounting for 19.1% of the full band, and its runtime was 1.6 min. It can be seen that miRF greatly reduces the running time of the algorithm. The characteristic wavelengths selected by iRF and miRF methods were mainly distributed in 1 582 nm-1 655 nm, followed by 1 160 nm-1 190 nm and 1 353 nm-1 383 nm, indicating that above regions were sensitive bands to tomato hardness. In order to prove the effectiveness of the proposed algorithm, the results of miRF-PLSR were compared with those of iRF-PLSR and SPA-PLSR. The prediction set correlation coefficients (RP) of the SPA-PLSR model and the iRF-PLSR model were 0.803 9 and 0.936 6 respectively. And the RP of miRF-PLSR model was 0.968 5. The root mean square error (RMSEP) of the SPA-PLSR model and the iRF-PLSR model were 0.007 7 kg/mm2 and 0.004 4 kg/mm2respectively. And the RMSEP of miRF-PLSR model was 0.004 0 kg/mm2. The experiments results show that the miRF-PLSR model has the best prediction results in all models. [ABSTRACT FROM AUTHOR]

Navel orange is a very popular fruit in China, which is mainly cultivated along the Yangtze River. Navel oranges are classified into different grades based on external quality and internal quality before they are sold. Soluble solids content is one of the main indices for evaluating the internal quality of navel orange. Therefore, it is very important to improve the detection accuracy of soluble solids content in production. So far, visible and near infrared spectroscopy (Vis-NIR) is one of the most widely used and effective techniques in internal quality assessment of fruits. In this study, 199 Fukumoto navel oranges were taken as experimental samples. The transmission spectra of navel oranges of three positions including pedicle upwards (P1), pedicle horizontal (P2) and pedicle downward (P3) were acquired by using a special visible and near infrared transmission spectrum measurement system designed by ourselves. The average spectra (P4) and weighted spectra (P5) of P1, P2 and P3 were calculated. The transmission spectra, including P1, P2, P3, P4 and P5 were preprocessed by multivariate scattering correction, standard normal variate transformation, first derivative and second derivative respectively. The best pretreatment results were obtained based on first derivative after comparative study. Then the spectra data preprocessed by first derivative were divided into 30 to 50 intervals with step length of 5, and backward interval partial least squares was used to select the optimal band combination. Good results observed when P1, P2, P3, P4 and P5 were divided into 35, 40, 30, 35 and 40 intervals, in which 161, 180, 114, 308 and 170 variables were retained. On this basis, competitive adaptive re-weighted sampling (CARS) was used to select feature variables. After running CARS for 20 times in each selection, 24, 23, 18, 39 and 22 variables were kept respectively. Finally, Five PLS models were established, including P1-PLS, P2-PLS, P3-PLS, P4-PLS and P5-PLS. Among the P1-PLS, P2-PLS and P3-PLS models, P2-PLS model was the best one, as the value of correlation coefficients of prediction was 0.924 and the value of root mean square error of prediction was 0.404. This model can be realized by adjusting the navel oranges to pedicle horizontal in modeling. P4-PLS model and P5-PLS model had achieved good prediction results, as the value of correlation coefficients of prediction was higher than 0.91 and the value of root mean square error of prediction was lower than 0.43. P4-PLS model was based on the average spectra of P1, P2 and P3, and had potential to be realized by rolling the navel oranges in actual application. However, P5-PLS model was based on weighted spectra of P1, P2 and P3, which was difficult to realize in on-line detection. This study can provide a reference for the development of on-line detection equipment for the assessment of internal content of substances in navel orange. [ABSTRACT FROM AUTHOR]

The Yellow River Delta is a typical eco-environmental fragile region where soil quality is not high and salinization is universal, and it is of great significance for the rehabilitation and reconstruction of degraded soil to know the situation of degradation soil quickly and accurately in the region. This research chose Kenli County in the Yellow River Delta as the research area and the measured data in 2008 as the basis, and the comprehensive evaluation of soil degradation was carried out based on soil degradation composite index, which was built by weighted combination of soil degradation evaluation index weight and membership value. Using TM image that was at the same time as the measured data, the spectral characteristics of different soil degradation levels were researched, and the correlations between soil degradation composite index and the bands' gray values were analyzed; with the combination of qualitative and quantitative methods, the sensitive bands were screened. Then the sensitive spectral indices of soil degradation were built, based on which the remote sensing inversion model of soil degradation was established, and the exponential model was selected ultimately which had the highest fitting precision. Verified on the accuracy and the inversion results, the model was applied to the remote sensing images in 2011 and 2013, and the soil degradation dynamics were analyzed during 2008-2013 at last. The results showed the lower-degree degradation soil in the study area was far away from the ocean, and degradation degree of soil in close distance from ocean was relatively high; taken altogether, the extent of soil degradation from the coast to the inland in the study area showed a trend from high to low. The sensitive bands were TM1, TM2 and TM3, and the spectral indices of soil degradation based on the combination of these 3 sensitive bands were characteristic spectral indices of soil degradation. The regression function models were built by soil degradation spectral indices and soil degradation composite index, and the exponential model was the best, which had the highest F value and the best fitting. The remote sensing inversion model of exponential form based on the characteristic spectral indices of soil degradation had more higher accuracy, whose R² was 0.7182, and validation RMSE, relative error and determination coefficient were 0.0241, 3.66% and 0.6724, respectively. By contrast, the inversion results were consistent with the comprehensive evaluation results based on the measured data in the same year; in particular, the inversion results were more ideal in the area where the land types were relatively simple and the spectral information was relatively clear. The condition of soil degradation changed little in the study area during 2008-2013, and the specific performances were that the area of mild degradation soil increased and degradation soil at other grades decreased, a small amount of moderate degradation soil turned into mild, and there was a gradual improvement in the trend as a whole. This study provides technical support for monitoring the degradation soil in the Yellow River Delta, and provides decision-making basis for the sustainable utilization and protection of land resources in this area. [ABSTRACT FROM AUTHOR]