Introduction:Early childhood development(ECD)centres are important community hubs in South Africa and act as sites for community detection of childhood nutrition problems.This study aimed to assess the ability of trai...Introduction:Early childhood development(ECD)centres are important community hubs in South Africa and act as sites for community detection of childhood nutrition problems.This study aimed to assess the ability of trained ECD practitioners with optimal support to correctly classify the nutritional status of infants and young children at ECD centres in the Nelson Mandela Bay.Methods:A descriptive,cross-sectional study was used to collect data from 1645 infants and children at 88 ECD centres.Anthropometric measurements were taken by trained fieldworkers and growth monitoring and promotion infrastructure was audited at ECD centres.Results:Of the sample,4.4%(n=72)were underweight by weight for age Z-score(WAZ<-2)and 0.8%(n=13)were severely underweight(WAZ<-3).Results showed that 13.1%(n=214)were stunted by height for age Z-score(HAZ<-2)and 4.5%(n=74)were severely stunted(HAZ<-3).The prevalence of moderate acute malnutrition was 1.2%and severe acute malnutrition was 0.5%,while the prevalence of overweight was 9.2%and the prevalence of obesity was 4%.A significant level of agreement between the correct interpretation and the ECD practitioners'interpretation was observed across all the anthropometric indicators investigated.The true positive wasting cases had a mean mid-upper arm circumference(MUAC)of 14.6 cm,which may explain the high false negative rate found in terms of children identified with wasting,where ECD practitioners fail to use the weight for height Z-score(WHZ)interpretation for screening.Conclusion:By using ECD centres as hub to screen for malnutrition,it may contribute to the early identification of failure to thrive among young children.Although it was concerning that trained ECD practitioners are missing some children with an unacceptably high false negative rate,it may have been due to the fact that wasting in older children cannot be identified with MUAC alone and that accurate WFH plotting is needed.Onsite mentorship by governmental health workers may provide ECD practitioners with more confidence to screen children for growth failure based on regular WFH measurements.Moreover,ECD practitioners will be more confident to monitor the Road to Health booklets for missed vaccinations,vitamin A and deworming opportunities.展开更多
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 pr...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.展开更多
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 n...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 2/3<sup>rd</sup> 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.展开更多
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 Stat...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展开更多
BACKGROUND Syngeneic orthotopic tumor models offer an optimal functional tumor–immune interface for hepatocellular carcinoma research.Yet,unpredictable growth kinetics and spontaneous regression pose major obstacles....BACKGROUND Syngeneic orthotopic tumor models offer an optimal functional tumor–immune interface for hepatocellular carcinoma research.Yet,unpredictable growth kinetics and spontaneous regression pose major obstacles.Efficient induction protocols and continuous monitoring are therefore essential.Routine exploratory surgeries are ethically untenable,making non-invasive imaging modalities attractive alternatives.High-resolution magnetic resonance imaging and microcomputed tomography deliver detailed insights but incur substantial equipment costs,radiation risks,time demands,and require specialized expertise—challenges that limit their routine use.In contrast,ultrasound(US)imaging emerges as a cost-effective,radiation-free,and rapid approach,facilitating practical and ethical longitudinal assessment of tumor progression in preclinical studies.AIM To optimize the orthotopic hepatocellular carcinoma model and evaluate the potential of US imaging for accurate and cost-effective tumor monitoring.METHODS Hepatocellular carcinoma was induced in 28 Sprague Dawley rats by implanting 5×10^(6) N1S1 cells into the left lateral hepatic lobe.Tumor progression was monitored weekly via US.Upon reaching 100-150 mm^(3),an experimental group(n=14)received Sorafenib(40 mg/kg)orally on alternate days for 28 days;efficacy was compared to untreated controls.US accuracy was validated against micro-computed tomography,gross caliper measurements and histopathological analysis.Reliability and operator proficiency in US assessment were also evaluated.RESULTS US images procured 7-day post-surgery revealed a well-defined hypoechoic nodule at the left liver lobe tip,confirming successful tumor induction(mean volume 130±39 mm^(3)).Only three animals exhibited spontaneous regression by week 2,underscoring the model’s stability.Sorafenib treatment elicited a marked tumor reduction(678±103 mm^(3))vs untreated control(6005±1760 mm^(3)).US assessment demonstrated robust intra and interobserver reproducibility with high sensitivity and specificity for tumor detection.Moreover,US derived volumes correlated strongly with gross caliper measurements,histopathological analysis,and microcomputed tomography imaging,validating its reliability as a non-invasive monitoring tool in preclinical hepatocellular carcinoma studies.CONCLUSION The results demonstrate that US imaging is a reliable,cost-effective,and animal sparing approach with an easy tomaster protocol,enabling monitoring of tumor progression and therapeutic response in orthotopic liver tumor models.展开更多
Smart agriculture utilizes sensors and software to control agricultural production through mobile or computer platforms,enabling unmanned,automated,and intelligent management.Recently,research and development in plant...Smart agriculture utilizes sensors and software to control agricultural production through mobile or computer platforms,enabling unmanned,automated,and intelligent management.Recently,research and development in plant growth monitoring technologies have garnered significant attention.The challenge lies in achieving long-term monitoring,phased predictions,and plant self-regulation without harming the plants.The present study demonstrates the fabrication of plant-compatible and breathable tensile and bending strain sensors using composite nanofiber membranes(CNMs)composed of Ti_(2)C_(2)T_(x)(MXene),carbon nanotubes(CNTs),and thermoplastic polyurethanes(TPU)through electrospinning and ultrasonic immersion techniques.The MXene and CNTs synergistically form a dual-network conductive structure on the TPU nanofiber membrane,thereby imparting the composite membrane with remarkable tensile sensitivity(5.41,7.39,and 3.39 within the ranges of 0%-20%,20%-50%,and 50%-70%,respectively)as well as exceptional bending sensitivity(1.79,0.89,and 0.46 within the ranges of 0°-30°,30°-90°,and 90°-120°,respectively).The tensile strain sensor,combined with a deep learning Long Short-Term Memory(LSTM)model,establishes a platform for plant growth monitoring and prediction.The bending strain sensor,integrated with a shape memory alloy(SMA)-based soft actuator,forms a plant sensing-actuating system to assist in plant leaf growth.This work leverages MXene/CNTs/TPU CNMs to flexibly prepare strain sensors for specific applications,combining deep learning and soft actuators to achieve plant growth prediction and self-regulation.This research holds significant importance in advancing the development of smart agriculture.展开更多
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 ...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.展开更多
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...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.展开更多
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...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.展开更多
基金Nelson Mandela University,London Metropolitan University and UNICEF。
文摘Introduction:Early childhood development(ECD)centres are important community hubs in South Africa and act as sites for community detection of childhood nutrition problems.This study aimed to assess the ability of trained ECD practitioners with optimal support to correctly classify the nutritional status of infants and young children at ECD centres in the Nelson Mandela Bay.Methods:A descriptive,cross-sectional study was used to collect data from 1645 infants and children at 88 ECD centres.Anthropometric measurements were taken by trained fieldworkers and growth monitoring and promotion infrastructure was audited at ECD centres.Results:Of the sample,4.4%(n=72)were underweight by weight for age Z-score(WAZ<-2)and 0.8%(n=13)were severely underweight(WAZ<-3).Results showed that 13.1%(n=214)were stunted by height for age Z-score(HAZ<-2)and 4.5%(n=74)were severely stunted(HAZ<-3).The prevalence of moderate acute malnutrition was 1.2%and severe acute malnutrition was 0.5%,while the prevalence of overweight was 9.2%and the prevalence of obesity was 4%.A significant level of agreement between the correct interpretation and the ECD practitioners'interpretation was observed across all the anthropometric indicators investigated.The true positive wasting cases had a mean mid-upper arm circumference(MUAC)of 14.6 cm,which may explain the high false negative rate found in terms of children identified with wasting,where ECD practitioners fail to use the weight for height Z-score(WHZ)interpretation for screening.Conclusion:By using ECD centres as hub to screen for malnutrition,it may contribute to the early identification of failure to thrive among young children.Although it was concerning that trained ECD practitioners are missing some children with an unacceptably high false negative rate,it may have been due to the fact that wasting in older children cannot be identified with MUAC alone and that accurate WFH plotting is needed.Onsite mentorship by governmental health workers may provide ECD practitioners with more confidence to screen children for growth failure based on regular WFH measurements.Moreover,ECD practitioners will be more confident to monitor the Road to Health booklets for missed vaccinations,vitamin A and deworming opportunities.
基金supported by The Research Grants Council,Hong Kong:Competitive Earmarked Research Grant,No.461907
文摘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.
文摘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 2/3<sup>rd</sup> 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.
文摘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
基金Supported by Amrita Vishwa Vidyapeetham Seed Grant,No.K-PHAR-24-722DST INSPIRE Fellowship,No.IF190226.
文摘BACKGROUND Syngeneic orthotopic tumor models offer an optimal functional tumor–immune interface for hepatocellular carcinoma research.Yet,unpredictable growth kinetics and spontaneous regression pose major obstacles.Efficient induction protocols and continuous monitoring are therefore essential.Routine exploratory surgeries are ethically untenable,making non-invasive imaging modalities attractive alternatives.High-resolution magnetic resonance imaging and microcomputed tomography deliver detailed insights but incur substantial equipment costs,radiation risks,time demands,and require specialized expertise—challenges that limit their routine use.In contrast,ultrasound(US)imaging emerges as a cost-effective,radiation-free,and rapid approach,facilitating practical and ethical longitudinal assessment of tumor progression in preclinical studies.AIM To optimize the orthotopic hepatocellular carcinoma model and evaluate the potential of US imaging for accurate and cost-effective tumor monitoring.METHODS Hepatocellular carcinoma was induced in 28 Sprague Dawley rats by implanting 5×10^(6) N1S1 cells into the left lateral hepatic lobe.Tumor progression was monitored weekly via US.Upon reaching 100-150 mm^(3),an experimental group(n=14)received Sorafenib(40 mg/kg)orally on alternate days for 28 days;efficacy was compared to untreated controls.US accuracy was validated against micro-computed tomography,gross caliper measurements and histopathological analysis.Reliability and operator proficiency in US assessment were also evaluated.RESULTS US images procured 7-day post-surgery revealed a well-defined hypoechoic nodule at the left liver lobe tip,confirming successful tumor induction(mean volume 130±39 mm^(3)).Only three animals exhibited spontaneous regression by week 2,underscoring the model’s stability.Sorafenib treatment elicited a marked tumor reduction(678±103 mm^(3))vs untreated control(6005±1760 mm^(3)).US assessment demonstrated robust intra and interobserver reproducibility with high sensitivity and specificity for tumor detection.Moreover,US derived volumes correlated strongly with gross caliper measurements,histopathological analysis,and microcomputed tomography imaging,validating its reliability as a non-invasive monitoring tool in preclinical hepatocellular carcinoma studies.CONCLUSION The results demonstrate that US imaging is a reliable,cost-effective,and animal sparing approach with an easy tomaster protocol,enabling monitoring of tumor progression and therapeutic response in orthotopic liver tumor models.
基金supported by the National Natural Science Foundation of China (62301291)Taishan Scholars Project Special Funds (tsqn202312035)。
文摘Smart agriculture utilizes sensors and software to control agricultural production through mobile or computer platforms,enabling unmanned,automated,and intelligent management.Recently,research and development in plant growth monitoring technologies have garnered significant attention.The challenge lies in achieving long-term monitoring,phased predictions,and plant self-regulation without harming the plants.The present study demonstrates the fabrication of plant-compatible and breathable tensile and bending strain sensors using composite nanofiber membranes(CNMs)composed of Ti_(2)C_(2)T_(x)(MXene),carbon nanotubes(CNTs),and thermoplastic polyurethanes(TPU)through electrospinning and ultrasonic immersion techniques.The MXene and CNTs synergistically form a dual-network conductive structure on the TPU nanofiber membrane,thereby imparting the composite membrane with remarkable tensile sensitivity(5.41,7.39,and 3.39 within the ranges of 0%-20%,20%-50%,and 50%-70%,respectively)as well as exceptional bending sensitivity(1.79,0.89,and 0.46 within the ranges of 0°-30°,30°-90°,and 90°-120°,respectively).The tensile strain sensor,combined with a deep learning Long Short-Term Memory(LSTM)model,establishes a platform for plant growth monitoring and prediction.The bending strain sensor,integrated with a shape memory alloy(SMA)-based soft actuator,forms a plant sensing-actuating system to assist in plant leaf growth.This work leverages MXene/CNTs/TPU CNMs to flexibly prepare strain sensors for specific applications,combining deep learning and soft actuators to achieve plant growth prediction and self-regulation.This research holds significant importance in advancing the development of smart agriculture.
基金supported by the R&D Program of Fundamental Technology and Utilization of Social Big Data by the National Institute of Information and Communications Technology(NICT),Japan.
文摘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.
基金supported by China’s Special Funds for Major State Basic Research Project(2013CB733405)the Fundamental Research Funds for the Central Universities(ZYGX2012J153 and ZYGX2012Z005)+1 种基金the Open Fund of the State Key Laboratory of Remote Sensing Science(OFSLRSS201408)the National Natural Science Foundation of China(40801130).
文摘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.
基金This research was supported by National Natural Science Foundation of Chinar for the project of Growth process monitoring of corn by combining time series spectral remote sensing images and terrestrial laser scanning data(41671433)Dynamic calibration of exterior orientations for vehicle laser scanner based structure features(41371434)Estimating the leaf area index of corn in whole growth period using terrestrial LiDAR data(41371327).
文摘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.
