Indication of the Expression of Transgene in Rice Plant Based on Hyperspectral Remote Sensing Technique Ⅱ——Growth Monitoring of Samples in the Contrast Experiment

Since the complication of monitoring and evaluating the problems about the transgenic expression and its impacts on the receptor in the transgenic crop breeding and other relevant evaluated works,the authors in the present work tried to assess the differences of spectral parameters of the transgenic rice in contrast with its parent group quantitatively and qualitatively,fulfilling the growth monitoring of the transgenic samples.The spectral parameters(spectral morphological characteristics and indices) chosen are highly related to internal or external stresses to the receipts,and thus could be applied as indicators of biophysical or biochemical processes changes of plant.By ASD portable field spectroradiometer with high-density probe,fine foliar spectra of 8 groups were obtained.By analyzing spectral angle and continuum removal,the spectral morphological differences and their locations of sample spectra were found which could be as auxiliary priori knowledge for quantitative analysis.By investigating spectral indices of the samples,the quantitative differences of spectra were revealed about foliar chlorophyll a+b and carotenoid content.In this study both the spectral differences between transgenic and parent groups and among transgenic groups were investigated.The results show that hyperspectral technique is promising and a helpful auxiliary tool in the study of monitoring the transgenic crop and other relevant researches.By this technique,quantitative and qualitative results of sample spectra could be provided as prior knowledge,as certain orientation,for laboratory professional advanced transgenic breeding study.

Nutritional Outcome and Growth Monitoring in Under-Five Children of Severe Acute Malnutrition with and without Edema in Bangladesh: A Five-Year Retrospective Review of Facility-Based Inpatient Care

Introduction: Severe acute malnutrition (SAM) is an important cause of death in children. Bangladesh has a huge burden of SAM in under-five children, but documentation of their protocolized management and outcome is not so frequent. Objective: Our aim was to identify the pattern of the nutritional outcome and growth monitoring of 0-59 months old children with severe acute malnutrition treated with identified medical complications where the presence or absence of edema is an important clinical factor. Methods: This was a facility-based retrospective observational study that was conducted in the Severe Acute Malnutrition block of Chittagong Medical College Hospital, Chittagong. Here, a total of 485 patients were admitted during the period from 2013 to 2017. Based on WHO & National guidelines, admission and discharge criteria were considered and determined. A structured and prescribed data format was prepared and data were collected from the hospital records. Daily clinical follow-ups and weight monitoring of the patients were also documented. Both descriptive and analytic analyses were executed. After Data collection, it was cleaned, edited, and stored in excel, epi-INFO, and analyzed by SPSS. P-value < 0.05 was considered to be statistically significant. Results: 54.84% of the admitted patients were cured and discharged during the study period. The mean age of the observed patients was 22.35 ± 15.8607 months. The majority of the patients came from rural areas and about 50% of them belonged to lower-middle-class families. The median weight gain of the children at SAM block during the clinical review was found to be moderate (7.35g/kg/day). About 2/3rd of the admitted patients stayed in the hospital for two weeks. The mean duration of hospital stay (in days) of the patients with edema (15.64 ± SD 7.133 days) was higher than that of the patients without edema (9.47 ± SD 5.881 days). 4.3% of patients did not gain weight during their hospital stay, and overall 8.04% of patients died during this period. Conclusion: More than half of the admitted patients showed moderate to good weight gain during their hospital stay. Non-edematous patients started to gain weight early and their mean weight gain was also higher. A greater portion of patients who had edema was cured (117, 81.8%) but defaulter & death rates, where contributed to a significant overall outcome (188, 38.76%), were more in non-edematous patients.

CAS Leading Crop Growth Monitoring

The Institute of Remote Sens-ing Applications (IRSA), apart of the Chinese Academyof Sciences (CAS), has been as-sessed as up to the world’s advancedlevel in large-scale crop monitoringby experts from the United Statesand Europe. At a recent conference jointlysponsored by CAS, the NationalAgricultural Statistics

Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle

Recently near-ground remote sensing using unmanned aerial vehicles(UAV)witnessed wide applications in obtaining field information.In this research,four Rapideye satellite images and eight RGB images acquired from UAV were used from early June to the end of July,2015 covering two experimental winter wheat fields,in order to monitor wheat canopy growth status and analyze the correlation among satellite images based normalized difference vegetation index(NDVI)with UAV’s RGB images based visible-band difference vegetation index(VDVI)and ground variables of the sampled grain protein contents.Firstly,through image interpretation of UAV’s multi-temporal RGB images with fine spatial resolution,the wheat canopy color changes could be intuitively and clearly monitored.Subsequently,by monitoring the changes of satellite images based NDVI as well as VDVI values and UAV’s RGB images based VDVI values,the conclusions were made that these three vegetation indices demonstrated the same and synchronized trend of increasing at the early stage of wheat growth season,reaching up to peak values at the same timing,and starting to decrease since then.The results of the correlation analysis between NDVI of satellite images and sampled grain protein contents show that NDVI has good predicative capability for mapping grain protein content before ripening growth stage around June7,2015,while the reliability of using satellite image based NDVI to predict grain protein contents becomes worse as ripening stage approaches.The regression analysis between UAV’s RGB image based VDVI and satellite image based VDVI as well as NDVI showed good coefficients of determination.It is concluded that it is feasible and practical to temporally complement satellite remote sensing by using UAV’s RGB images based vegetation indices to monitor wheat growth status and to map within-field spatial variations of grain protein contents for small scale farmlands.

Monitoring paddy rice phenology using time series MODIS data over Jiangxi Province,China

Paddy rice is one of the most important crops in the world.Accurate estimation and monitoring of paddy rice phenology is necessary for management and yield prediction.Remotely sensed time-series data are essential for estimation of crop phenology stages across large areas.Here,the paddy rice phenological stages(i.e.,transplanting,tillering,heading,and harvesting)were detected in Jiangxi Province,China.A comparison study was conducted using ground observation data from 10 agricultural meteorological stations,collected between 2006 and 2008.The phenological stages were detected using Moderate Resolution Imaging Spectroradiometer(MODIS)time-series enhanced vegetation index(EVI)data.Savitzky-Golay filter and wavelet transform were used to reduce the noise in the time-series EVI data and reconstruct the smoothed EVI time-series profile.Key phenological stages of double-cropping rice were detected using the characteristics of the smoothed EVI profile.The root mean square errors(RMSEs)for each stage were ±10 days around the ground observation data.The results suggest that Savitzky-Golay filter and wavelet transform are promising approaches for reconstructing high-quality EVI time-series data.Moreover,the phenological stages of double-cropping rice could be detected using time-series MODIS EVI data smoothed by Savitzky-Golay filter and wavelet transform.

Automated extraction of corn leaf points from unorganized terrestrial LiDAR point clouds

Terrestrial LiDAR data can be used to extract accurate structure parameters of corn plant and canopy,such as leaf area,leaf distribution,and 3D model.The first step of these applications is to extract corn leaf points from unorganized LiDAR point clouds.This paper focused on an automated extraction algorithm for identifying the points returning on corn leaf from massive,unorganized LiDAR point clouds.In order to mine the distinct geometry of corn leaves and stalk,the Difference of Normal(DoN)method was proposed to extract corn leaf points.Firstly,the normals of corn leaf surface for all points were estimated on multiple scales.Secondly,the directional ambiguity of the normals was eliminated to obtain the same normal direction for the same leaf distribution.Finally,the DoN was computed and the computed DoN results on the optimal scale were used to extract leave points.The quantitative accuracy assessment showed that the overall accuracy was 94.10%,commission error was 5.89%,and omission error was 18.65%.The results indicate that the proposed method is effective and the corn leaf points can be extracted automatically from massive,unorganized terrestrial LiDAR point clouds using the proposed DoN method.