Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing technology.Despite the differences in LULC classification schemes,there is a lack of researc...Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing technology.Despite the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified framework.Thus,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification agreement.The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,respectively.The FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,respectively.High consistency was mainly observed in the Northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,China.In contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern China.Field size was the most important factor for mapping cropland.For area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and GlobCover2009.The cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products.Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction,which is essential for food security and crop management,so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.展开更多
Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effect...Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.展开更多
Drought is the major detrimental environmental factor for wheat(Triticum aestivum L.)production.The exploration of genetic patterns underlying drought tolerance is of great significance.Here we report the gene actions...Drought is the major detrimental environmental factor for wheat(Triticum aestivum L.)production.The exploration of genetic patterns underlying drought tolerance is of great significance.Here we report the gene actions controlling the phenological traits using the line×tester model studying 27 crosses and 12 parents under normal irrigation and drought conditions.The results interpreted via multiple analysis(mean performance,correlations,principal component,genetic analysis,heterotic and heterobeltiotic potential)disclosed highly significant differences among germplasm.The phenological waxiness traits(glume,boom,and sheath)were strongly interlinked.Flag leaf area exhibits a positive association with peduncle and spike length under drought.The growing degree days(heat-units)greatly influence spikelets and grains per spike,however,the grain yield/plant was significantly reduced(17.44 g to 13.25 g)under drought.The principal components based on eigenvalue indicated significant PCs(first-seven)accounted for 79.9%and 73.9%of total variability under normal irrigation and drought,respectively.The investigated yield traits showed complex genetic behaviour.The genetic advance confronted a moderate to high heritability for spikelets/spike and grain yield/plant.The traits conditioned by dominant genetic effects in normal irrigation were inversely controlled by additive genetic effects under drought and vice versa.The magnitude of dominance effects for phenological and yield traits,i.e.,leaf twist,auricle hairiness,grain yield/plant,spikelets,and grains/spike suggests that selection by the pedigree method is appropriate for improving these traits under normal irrigation conditions and could serve as an indirect selection index for improving yield-oriented traits in wheat populations for drought tolerance.However,the phenotypic selection could be more than effective for traits conditioned by additive genetic effects under drought.We suggest five significant cross combinations based on heterotic and heterobeltiotic potential of wheat genotypes for improved yield and enhanced biological production of wheat in advanced generations under drought.展开更多
Over the past five decades, increased pressure caused by the rapidly growing population has resulted in a reclamation of agricultural and urban buffer zones along China's coastline. However, information about the ...Over the past five decades, increased pressure caused by the rapidly growing population has resulted in a reclamation of agricultural and urban buffer zones along China's coastline. However, information about the spatio–temporal variation of soil salinity in these reclaimed regions is limited. As such, obtaining this information is crucial for mapping the variation in saline areas and to identify suitable salinity management strategies. In this study, we employed EM38 data to conduct digital soil mapping of spatio–temporal variation and map these variations of different site-specific zones. The results indicated that the distribution of soil salinity was heterogeneous in the middle of, and that the leaching of salts was significant at the edges of, the study field. Afterwards, fuzzy-k means algorithm was used to divide the site-specific management zones within the time series apparent soil electrical conductivity(ECa) data and the spatial correlations of variation. We concluded that two management zones are optimal to guide precision management. Zone A had an average salinity level of about 165 mS m–1, in which salt-tolerant crops, such as cotton and barley can grow normally, while crops such as soybean and cowpeas may be planted using leaching and increasing the mulching film methods to reduce the accumulation of salt in surface soil. In Zone B, there was a low salinity level with a mean of 89 mS m–1 for ECa, which allows for rice, wheat, and a wide range of vegetables to be grown normally. In such situations, measures such as an optimized combination of irrigation and drainage, as well as soil amendment can be taken to adjust and control the salt content. Particularly, flattening the land with a large-scale machine was used to improve the ability of micro-topography to influence salt migration; rice and other dry, land crops were planted in rotation in combination with utilizing salt-leaching multiple times to speed up desalinization.展开更多
The water content in vegetative leaves is an important indicator to plant science.It reveals the physiological status of plants and provides valuable information in irrigation management.Terahertz(THz)as a state-of-th...The water content in vegetative leaves is an important indicator to plant science.It reveals the physiological status of plants and provides valuable information in irrigation management.Terahertz(THz)as a state-of-the-art technology shows great potential in measuring and monitoring the water status in plant leaves.This paper reviewed the theoretical models for calculating water content in the plant leaves,the methods for eliminating the scattering loss caused by the surface roughness of leaf,the applications of THz spectroscopy and THz imaging for monitoring leaf water content and describing leaf water distribution.The survey of the researches presents the considerable advantages of this emerging and promising THz technology in agriculture.展开更多
Salinization is a threat to global agricultural and soil resource allocation.Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets(>250 m)and semiqualitat...Salinization is a threat to global agricultural and soil resource allocation.Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets(>250 m)and semiqualitative classification rules(five ranks).Based on these two limitations,we proposed a framework to quantitatively estimate global soil salt content in five climate regions at 10 m by integrating Sentinel-1/2 remotely sensed images,climate,parent material,terrain data,and machine learning.In hyper-arid and arid region,models established using Sentinel-2 and other geospatial data showed the highest accuracy with R^(2) of 0.85 and 0.62,respectively.In semi-arid,dry sub-humid,and humid regions,models performed best using Sentinel-1,Sentinel-2,and other geospatial data with R^(2) of 0.87,0.80,and 0.87,respectively.The accuracy of the global models is considerable with field validation in Iran and Xinjiang,and compared with digitized salinity maps in California,Brazil,Turkey,South Africa,and Shandong.The proportion of extremely saline soils in Europe is 10.21%,followed by South America(5.91%),Oceania(5.80%),North America(4.05%),Asia(1.19%),and Africa(1.11%).Climatic conditions,groundwater,and salinity index are key covariates in global soil salinity estimation.Use of radar data improves estimation accuracy in wet regions.The map of global soil salinity at 10 m provides a detailed,high-precision basis for soil property investigation and resource management.展开更多
Herbicides and heavy metals are hazardous substances of environmental pollution,resulting in plant stress and harming humans and animals.Identification of stress types can help trace stress sources,manage plant growth...Herbicides and heavy metals are hazardous substances of environmental pollution,resulting in plant stress and harming humans and animals.Identification of stress types can help trace stress sources,manage plant growth,and improve stress-resistant breeding.In this research,hyperspectral imaging(HSI)and chlorophyll fluorescence imaging(Chl-FI)were adopted to identify the rice plants under two types of herbicide stresses(butachlor(DCA)and quinclorac(ELK))and two types of heavy metal stresses(cadmium(Cd)and copper(Cu)).展开更多
The radiative transfer model,PROSPECT,has been widely applied for retrieving leaf biochemical traits.However,little work has been conducted to evaluate the stability of the PROSPECT model with consideration of multipl...The radiative transfer model,PROSPECT,has been widely applied for retrieving leaf biochemical traits.However,little work has been conducted to evaluate the stability of the PROSPECT model with consideration of multiple factors(i.e.,spectral resolution,signal-to-noise ratio,plant growth stages,and treatments).This study aims to investigate the stability of the PROSPECT model for retrieving leaf chlorophyll(Chl)content(Cab).Leaf hemispherical reflectance and transmittance of oilseed rape were acquired at different spectral resolutions,noise levels,growth stages,and nitrogen treatments.The Chl content was also measured destructively by using a microplate spectrophotometer.The performance of the PROSPECT model was compared with a commonly used random forest(RF)model.The results showed that the prediction accuracy of PROSPECT and RF models for Cab did not produce significant differences under varied spectral resolutions ranging from 1 to 20 nm.The ranges of the relative root mean square errors(rRMSE)of the PROSPECT and RF models were 12%-13%and 11.70%-12.86%,respectively.However,the performance of both models for leaf Chl retrieval was strongly influenced by the noise level with the rRMSE of 13-15.37%and 12.04%-15.80%for PROSPECT and RF,respectively.For different growth stages,the PROSPECT model had similar prediction accuracies(rRMSE=9.26%-12.41%)to the RF model(rRMSE=9.17%-12.70%).Furthermore,the superiority of the PROSPECT model(rRMSE=10.10%-12.82%)over the RF model(rRMSE=11.81%-15.47%)was prominently observed when tested with plants growth at different nitrogen treatment levels.The results demonstrated that the PROSPECT model has a more stable performance than the RF model for all datasets in this study.展开更多
Reasonable design of the parameters of thermal processing such as conditioning and cooling according to formula changes of pelleted feeds has always been a serious challenge for Chinese feed mills and feed equipment m...Reasonable design of the parameters of thermal processing such as conditioning and cooling according to formula changes of pelleted feeds has always been a serious challenge for Chinese feed mills and feed equipment manufacturers. Studying the thermophysical properties of different protein feeds under different temperatures and particle sizes will facilitate the equipment design, parameter optimization, and simulation for the thermal processing of pelleted feeds. In this study, the specific heat (Cp), thermal conductivity (kb), and thermal diffusivity (α) of six plant protein supplements with three particle sizes were determined over a temperature range of 25℃-100℃. The differences in Cp, kb, and α among different feedstuffs and particle sizes were analyzed and the influences of temperature and particle size on these properties were evaluated. Results showed that the Cp, kb, and α of all the feedstuffs increased with increasing temperature and varied from 1.622 to 2.417 kJ/(kg∙℃), 0.080 to 0.362 W/(m∙℃), 6.379×10^(-8) to 21.984×10^(-8) m^(2)/s, respectively. To rise to the same temperature, the distiller’s dried grain with solubles (DDGS) needed to absorb 3% more heat than that required for soybean meal (SBM), while the rest four feedstuffs just needed to absorb 93%-98% heat for SBM. Particle size had no significant effect on Cp for all the feedstuffs (p>0.05). However, descending trends in kb and α were observed with increasing particle size for a certain feedstuff at the same bulk density. In addition, regression equations with only statistically significant terms were developed to describe Cp, kb, and α as a function of temperature and particle size for six feedstuffs. The results can provide basic theory and data for the optimization of thermal processing parameters required for the plant-protein ingredient change in compound feed formulations.展开更多
Fusing three-dimensional(3D)and multispectral(MS)imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge.Acquiring high-quality 3D MS point clouds(3DM...Fusing three-dimensional(3D)and multispectral(MS)imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge.Acquiring high-quality 3D MS point clouds(3DMPCs)of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure.Here,we present a novel 3D spatial–spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field(NeREF)for radiometric calibration.This approach was used to acquire 3DMPCs of perilla,tomato,and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness(EWT)estimation.The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6%compared with the fixed viewpoints alone.The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error(RMSE)of 58.93%for extracted reflectance spectra.The RMSE for chlorophyll content and EWT predictions decreased by 21.25%and 14.13%using partial least squares regression with the generated 3DMPCs.Collectively,our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions,which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits,and thus will facilitate plant biology and genetic studies as well as crop breeding.展开更多
Rapid determination of chlorophyll content is significant for evaluating cotton’s nutritional and physiological status.Hyperspectral technology equipped with multivariate analysis methods has been widely used for chl...Rapid determination of chlorophyll content is significant for evaluating cotton’s nutritional and physiological status.Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection.However,the model developed on one batch or variety cannot produce the same effect for another due to variations,such as samples and measurement conditions.展开更多
基金supported by the National Key Research and Development Program of China(2022YFB3903503)the National Natural Science Foundation of China(U1901601)the Science and Technology Project of the Department of Education of Jiangxi Province,China(GJJ210541)。
文摘Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing technology.Despite the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified framework.Thus,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification agreement.The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,respectively.The FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,respectively.High consistency was mainly observed in the Northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,China.In contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern China.Field size was the most important factor for mapping cropland.For area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and GlobCover2009.The cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products.Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction,which is essential for food security and crop management,so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.
基金the National Natural Science Foundation of China(U1901601)the National Key Research and Development Program of China(2022YFB3903503)。
文摘Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.
文摘Drought is the major detrimental environmental factor for wheat(Triticum aestivum L.)production.The exploration of genetic patterns underlying drought tolerance is of great significance.Here we report the gene actions controlling the phenological traits using the line×tester model studying 27 crosses and 12 parents under normal irrigation and drought conditions.The results interpreted via multiple analysis(mean performance,correlations,principal component,genetic analysis,heterotic and heterobeltiotic potential)disclosed highly significant differences among germplasm.The phenological waxiness traits(glume,boom,and sheath)were strongly interlinked.Flag leaf area exhibits a positive association with peduncle and spike length under drought.The growing degree days(heat-units)greatly influence spikelets and grains per spike,however,the grain yield/plant was significantly reduced(17.44 g to 13.25 g)under drought.The principal components based on eigenvalue indicated significant PCs(first-seven)accounted for 79.9%and 73.9%of total variability under normal irrigation and drought,respectively.The investigated yield traits showed complex genetic behaviour.The genetic advance confronted a moderate to high heritability for spikelets/spike and grain yield/plant.The traits conditioned by dominant genetic effects in normal irrigation were inversely controlled by additive genetic effects under drought and vice versa.The magnitude of dominance effects for phenological and yield traits,i.e.,leaf twist,auricle hairiness,grain yield/plant,spikelets,and grains/spike suggests that selection by the pedigree method is appropriate for improving these traits under normal irrigation conditions and could serve as an indirect selection index for improving yield-oriented traits in wheat populations for drought tolerance.However,the phenotypic selection could be more than effective for traits conditioned by additive genetic effects under drought.We suggest five significant cross combinations based on heterotic and heterobeltiotic potential of wheat genotypes for improved yield and enhanced biological production of wheat in advanced generations under drought.
基金funded by the National Natural Science Foundation of China (41601213)the National Key Research and Development Program of China (2017YFD0700501)the Major Science and Technology Projects of Henan, China (171100110600)
文摘Over the past five decades, increased pressure caused by the rapidly growing population has resulted in a reclamation of agricultural and urban buffer zones along China's coastline. However, information about the spatio–temporal variation of soil salinity in these reclaimed regions is limited. As such, obtaining this information is crucial for mapping the variation in saline areas and to identify suitable salinity management strategies. In this study, we employed EM38 data to conduct digital soil mapping of spatio–temporal variation and map these variations of different site-specific zones. The results indicated that the distribution of soil salinity was heterogeneous in the middle of, and that the leaching of salts was significant at the edges of, the study field. Afterwards, fuzzy-k means algorithm was used to divide the site-specific management zones within the time series apparent soil electrical conductivity(ECa) data and the spatial correlations of variation. We concluded that two management zones are optimal to guide precision management. Zone A had an average salinity level of about 165 mS m–1, in which salt-tolerant crops, such as cotton and barley can grow normally, while crops such as soybean and cowpeas may be planted using leaching and increasing the mulching film methods to reduce the accumulation of salt in surface soil. In Zone B, there was a low salinity level with a mean of 89 mS m–1 for ECa, which allows for rice, wheat, and a wide range of vegetables to be grown normally. In such situations, measures such as an optimized combination of irrigation and drainage, as well as soil amendment can be taken to adjust and control the salt content. Particularly, flattening the land with a large-scale machine was used to improve the ability of micro-topography to influence salt migration; rice and other dry, land crops were planted in rotation in combination with utilizing salt-leaching multiple times to speed up desalinization.
基金This work was supported by the National Key Point Research and Invention Program of the Thirteenth(2016YFD0700304)the National Key Research&Development program of China(2016YFD0300606 and 2017YFD0700501).
文摘The water content in vegetative leaves is an important indicator to plant science.It reveals the physiological status of plants and provides valuable information in irrigation management.Terahertz(THz)as a state-of-the-art technology shows great potential in measuring and monitoring the water status in plant leaves.This paper reviewed the theoretical models for calculating water content in the plant leaves,the methods for eliminating the scattering loss caused by the surface roughness of leaf,the applications of THz spectroscopy and THz imaging for monitoring leaf water content and describing leaf water distribution.The survey of the researches presents the considerable advantages of this emerging and promising THz technology in agriculture.
基金supported by the National Key Research and Development Program(grant numbers 2018YFE0107000 and 2023YFD1900102)the National Science Foundation of China(grant numbers 42261016 and 41061031)+4 种基金the Bingtuan Science and Technology Program(grant number 2020CB032)the Tarim University President’s Fund(grant number TDZKCX202205)the China Scholarship Council(CSC)the Academic Rising Star Program for Doctoral Students of Zhejiang Universitythe Outstanding Ph.D.Dissertation Funding of Zhejiang University.
文摘Salinization is a threat to global agricultural and soil resource allocation.Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets(>250 m)and semiqualitative classification rules(five ranks).Based on these two limitations,we proposed a framework to quantitatively estimate global soil salt content in five climate regions at 10 m by integrating Sentinel-1/2 remotely sensed images,climate,parent material,terrain data,and machine learning.In hyper-arid and arid region,models established using Sentinel-2 and other geospatial data showed the highest accuracy with R^(2) of 0.85 and 0.62,respectively.In semi-arid,dry sub-humid,and humid regions,models performed best using Sentinel-1,Sentinel-2,and other geospatial data with R^(2) of 0.87,0.80,and 0.87,respectively.The accuracy of the global models is considerable with field validation in Iran and Xinjiang,and compared with digitized salinity maps in California,Brazil,Turkey,South Africa,and Shandong.The proportion of extremely saline soils in Europe is 10.21%,followed by South America(5.91%),Oceania(5.80%),North America(4.05%),Asia(1.19%),and Africa(1.11%).Climatic conditions,groundwater,and salinity index are key covariates in global soil salinity estimation.Use of radar data improves estimation accuracy in wet regions.The map of global soil salinity at 10 m provides a detailed,high-precision basis for soil property investigation and resource management.
基金This study was supported by the Shenzhen Science and Technology Projects(CJGJZD20210408092401004)the National Natural Science Foundation of China(61705195).
文摘Herbicides and heavy metals are hazardous substances of environmental pollution,resulting in plant stress and harming humans and animals.Identification of stress types can help trace stress sources,manage plant growth,and improve stress-resistant breeding.In this research,hyperspectral imaging(HSI)and chlorophyll fluorescence imaging(Chl-FI)were adopted to identify the rice plants under two types of herbicide stresses(butachlor(DCA)and quinclorac(ELK))and two types of heavy metal stresses(cadmium(Cd)and copper(Cu)).
基金supported by the National Natural Science Foundation of China(Grant No.31801256)National Key Research&Development Program supported by Ministry of Science and Technology of China(Grant No.2017YFD0201501).
文摘The radiative transfer model,PROSPECT,has been widely applied for retrieving leaf biochemical traits.However,little work has been conducted to evaluate the stability of the PROSPECT model with consideration of multiple factors(i.e.,spectral resolution,signal-to-noise ratio,plant growth stages,and treatments).This study aims to investigate the stability of the PROSPECT model for retrieving leaf chlorophyll(Chl)content(Cab).Leaf hemispherical reflectance and transmittance of oilseed rape were acquired at different spectral resolutions,noise levels,growth stages,and nitrogen treatments.The Chl content was also measured destructively by using a microplate spectrophotometer.The performance of the PROSPECT model was compared with a commonly used random forest(RF)model.The results showed that the prediction accuracy of PROSPECT and RF models for Cab did not produce significant differences under varied spectral resolutions ranging from 1 to 20 nm.The ranges of the relative root mean square errors(rRMSE)of the PROSPECT and RF models were 12%-13%and 11.70%-12.86%,respectively.However,the performance of both models for leaf Chl retrieval was strongly influenced by the noise level with the rRMSE of 13-15.37%and 12.04%-15.80%for PROSPECT and RF,respectively.For different growth stages,the PROSPECT model had similar prediction accuracies(rRMSE=9.26%-12.41%)to the RF model(rRMSE=9.17%-12.70%).Furthermore,the superiority of the PROSPECT model(rRMSE=10.10%-12.82%)over the RF model(rRMSE=11.81%-15.47%)was prominently observed when tested with plants growth at different nitrogen treatment levels.The results demonstrated that the PROSPECT model has a more stable performance than the RF model for all datasets in this study.
基金supported by the International S&T Cooperation Program of China (Grant No.2019YFE0103800).
文摘Reasonable design of the parameters of thermal processing such as conditioning and cooling according to formula changes of pelleted feeds has always been a serious challenge for Chinese feed mills and feed equipment manufacturers. Studying the thermophysical properties of different protein feeds under different temperatures and particle sizes will facilitate the equipment design, parameter optimization, and simulation for the thermal processing of pelleted feeds. In this study, the specific heat (Cp), thermal conductivity (kb), and thermal diffusivity (α) of six plant protein supplements with three particle sizes were determined over a temperature range of 25℃-100℃. The differences in Cp, kb, and α among different feedstuffs and particle sizes were analyzed and the influences of temperature and particle size on these properties were evaluated. Results showed that the Cp, kb, and α of all the feedstuffs increased with increasing temperature and varied from 1.622 to 2.417 kJ/(kg∙℃), 0.080 to 0.362 W/(m∙℃), 6.379×10^(-8) to 21.984×10^(-8) m^(2)/s, respectively. To rise to the same temperature, the distiller’s dried grain with solubles (DDGS) needed to absorb 3% more heat than that required for soybean meal (SBM), while the rest four feedstuffs just needed to absorb 93%-98% heat for SBM. Particle size had no significant effect on Cp for all the feedstuffs (p>0.05). However, descending trends in kb and α were observed with increasing particle size for a certain feedstuff at the same bulk density. In addition, regression equations with only statistically significant terms were developed to describe Cp, kb, and α as a function of temperature and particle size for six feedstuffs. The results can provide basic theory and data for the optimization of thermal processing parameters required for the plant-protein ingredient change in compound feed formulations.
基金funded by the National Natural Science Foundation of China(32371985)the Fundamental Research Funds for the Central Universities,China(226-2022-00217).
文摘Fusing three-dimensional(3D)and multispectral(MS)imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge.Acquiring high-quality 3D MS point clouds(3DMPCs)of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure.Here,we present a novel 3D spatial–spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field(NeREF)for radiometric calibration.This approach was used to acquire 3DMPCs of perilla,tomato,and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness(EWT)estimation.The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6%compared with the fixed viewpoints alone.The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error(RMSE)of 58.93%for extracted reflectance spectra.The RMSE for chlorophyll content and EWT predictions decreased by 21.25%and 14.13%using partial least squares regression with the generated 3DMPCs.Collectively,our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions,which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits,and thus will facilitate plant biology and genetic studies as well as crop breeding.
基金This research was supported by XPCC Science and Technol-ogy Projects of Key Areas(2020AB005).
文摘Rapid determination of chlorophyll content is significant for evaluating cotton’s nutritional and physiological status.Hyperspectral technology equipped with multivariate analysis methods has been widely used for chlorophyll content detection.However,the model developed on one batch or variety cannot produce the same effect for another due to variations,such as samples and measurement conditions.
