The increase in the frequency and intensity of drought events expected in the coming decades in Western Europe may disturb forest biogeochemical cycles and create nutrient deficiencies in trees.One possible origin of ...The increase in the frequency and intensity of drought events expected in the coming decades in Western Europe may disturb forest biogeochemical cycles and create nutrient deficiencies in trees.One possible origin of nutrient deficiency is the disturbance of the partitioning of the green leaf pool during the leaf senescence period between resorption,foliar leaching and senesced leaves.However,the effects of drought events on this partitioning and the consequences for the maintenance of tree nutrition are poorly documented.An experiment in a beech forest in Meuse(France)was conducted to assess the effect of drought events on nutrient canopy exchanges and on the partitioning of the green leaf pool during the leaf senescence period.The aim was to identify potential nutritional consequences of droughts for trees.Monitoring nutrient dynamics,including resorption,chemistry of green and senesced leaves,foliar absorption and leaching in mature beech stands from 2012 to 2019 allowed us to compare the nutrient exchanges for three nondry and three dry years(i.e.,with an intense drought event during the growing season).During dry years,we observed a decrease by almost a third of the potassium(K)partitioning to resorption(i.e.resorption efficiency),thus reducing the K reserve in trees for the next growing season.This result suggests that with the increased drought frequency and intensity expected for the coming decades,there will be a risk of potassium deficiency in trees,as already observed in a rainfall exclusion experiment on the same study site.Reduced foliar leaching and higher parititioning to the senesced leaves for K and phosphorus(P)were also observed.In addition,a slight increase in nitrogen(N)resorption efficiency occurred during dry years which is more likely to improve tree nutrition.The calcium(Ca)negative resorption decreased,with no apparent consequence in our study site.Our results show that nutrient exchanges in the canopy and the partitioning of the green leaf pool can be modified by drought events,and may have consequences on tree nutrition.展开更多
Canopy temperature strongly influences crop yield formation and is closely related to plant physiological traits.However, the effects of nitrogen treatment on canopy temperature and rice growth have yet to be comprehe...Canopy temperature strongly influences crop yield formation and is closely related to plant physiological traits.However, the effects of nitrogen treatment on canopy temperature and rice growth have yet to be comprehensively examined. We conducted a two-year field experiment with three rice varieties(HD-5, NJ-9108, and YJ-805) and three nitrogen treatments(zero-N control(CK), 200 kg ha~(–1)(MN), and 300 kg ha~(–1)(HN)). We measured canopy temperature using a drone equipped with a high-precision camera at the six stages of the growth period. Generally,canopy temperature was significantly higher for CK than for MN and HN during the tillering, jointing, booting, and heading stages. The temperature was not significantly different among the nitrogen treatments between the milky and waxy stages. The canopy temperature of different rice varieties was found to follow the order: HD-5>NJ-9108>YJ-805, but the difference was not significant. The canopy temperature of rice was mainly related to plant traits, such as shoot fresh weight(correlation coefficient r=–0.895), plant water content(–0.912), net photosynthesis(–0.84), stomatal conductance(–0.91), transpiration rate(–0.90), and leaf stomatal area(–0.83). A structural equation model(SEM) showed that nitrogen fertilizer was an important factor affecting the rice canopy temperature.Our study revealed:(1) A suite of plant traits was associated with the nitrogen effects on canopy temperature,(2) the heading stage was the best time to observe rice canopy temperature, and(3) at that stage, canopy temperature was negatively correlated with rice yield, panicle number, and grain number per panicle. This study suggests that canopy temperature can be a convenient and accurate indicator of rice growth and yield prediction.展开更多
The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field samplin...The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.展开更多
A collection representing the native range of pecan was planted at the US DA-ARS Southeastern Fruit and Tree Nut Research Station,Byron,GA.The collection(867 trees)is a valuable genetic resource for characterizing imp...A collection representing the native range of pecan was planted at the US DA-ARS Southeastern Fruit and Tree Nut Research Station,Byron,GA.The collection(867 trees)is a valuable genetic resource for characterizing important horticultural traits.Canopy density during leaf fall is important as the seasonal canopy dynamics provides insights to environmental cues and breeding potential of germplasm.The ability of visual raters to estimate canopy density on a subset of the provenance collection(76 trees)as an indicator of leaf shed during autumn along with image analysis values was explored.Mean canopy density using the image analysis software was less compared to visual estimates(11.9%vs 18.4%,respectively).At higher canopy densities,the raters overestimated foliage density,but overall agreement between raters and measured values was good(ρc=0.849 to 0.915),and inter-rater reliability was high(R^(2)=0.910 to 0.953).The provenance from Missouri(MOL),the northernmost provenance,had the lowest canopy density in November,and results show that the higher the latitude of the provenance,the lower the canopy density.Based on regression,the source provenance latitude explained 0.609 of the variation using image analysis,and0.551 to 0.640 when based on the rater estimates of canopy density.Visual assessment of pecan canopy density due to late season leaf fall for comparing pecan genotypes provides accurate and reliable estimates and could be used in future studies of the whole provenance collection.展开更多
One of the most important objectives for breeders is to develop high-yield cultivars.The increase in crop yield has met with bottlenecks after the first green revolution,and more recent efforts have been focusing on a...One of the most important objectives for breeders is to develop high-yield cultivars.The increase in crop yield has met with bottlenecks after the first green revolution,and more recent efforts have been focusing on achieving high photosynthetic efficiency traits in order to enhance the yield.Leaf shape is a significant agronomic trait of upland cotton that affects plant and canopy architecture,yield,and other production attributes.The major leaf shape types,including normal,sub-okra,okra,and super-okra,with varying levels of lobe severity,are controlled by a multiple allelic series of the D-genome locus L-D_(1).To analyze the effects of L-D_(1)alleles on leaf morphology,photosynthetic related traits and yield of cotton,two sets of near isogenic lines(NILs)with different alleles were constructed in Lumianyan 22(LMY22)and Lumianyan 28(LMY28)backgrounds.The analysis of morphological parameters and the results of virus-induced gene silencing(VIGS)showed that the regulation of leaf shape by L-D_(1)alleles was similar to a gene-dosage effect.Compared with the normal leaf,deeper lobes of the sub-okra leaf improved plant canopy structure by decreasing the leaf area index(LAI)and increasing the light transmittance rate(LTR),and the mid-range LAI of sub-okra leaf also guaranteed the accumulation of cotton biomass.Although the chlorophyll content(SPAD)of sub-okra leaf was lower than those of the other two leaf shapes,the net photosynthetic rate(Pn)of sub-okra leaf was higher than those of okra leaf and normal leaf at most stages.Thus,the improvements in canopy structure,as well as photosynthetic and physiological characteristics,contributed to optimizing the light environment,thereby increasing the total biomass and yield in the lines with a sub-okra leaf shape.Our results suggest that the sub-okra leaf may have practical application in cultivating varieties,and could enhance sustainable and profitable cotton production.展开更多
Tree species diversity is vital for maintaining ecosystem functions,yet our ability to map the distribution of tree diversity is limited due to difficulties in traditional field-based approaches.Recent developments in...Tree species diversity is vital for maintaining ecosystem functions,yet our ability to map the distribution of tree diversity is limited due to difficulties in traditional field-based approaches.Recent developments in spaceborne remote sensing provide unprecedented opportunities to map and monitor tree diversity more efficiently.Here we built partial least squares regression models using the multispectral surface reflectance acquired by Sentinel-2 satellites and the inventory data from 74 subtropical forest plots to predict canopy tree diversity in a national natural reserve in eastern China.In particular,we evaluated the underappreciated roles of the practical definition of forest canopy and phenological variation in predicting tree diversity by testing three different definitions of canopy trees and comparing models built using satellite imagery of different seasons.Our best models explained 42%–63%variations in observed diversities in cross-validation tests,with higher explanation power for diversity indices that are more sensitive to abundant species.The models built using imageries from early spring and late autumn showed consistently better fits than those built using data from other seasons,highlighting the significant role of transitional phenology in remotely sensing plant diversity.Our results suggested that the cumulative diameter(60%–80%)of the biggest trees is a better way to define the canopy layer than using the subjective fixeddiameter-threshold(5–12 cm)or the cumulative basal area(90%–95%)of the biggest trees.Remarkably,these approaches resulted in contrasting diversity maps that call attention to canopy structure in remote sensing of tree diversity.This study demonstrates the potential of mapping and monitoring tree diversity using the Sentinal-2 data in species-rich forests.展开更多
Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evalua...Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evaluation of VW severity(VWS)forms the basis of field cotton VW control,which has great significance to cotton production.Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses,which require abundant time and professional expertise.Remote and proximal sensing using imagery and spectrometry have great potential for this purpose.In this study,we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values,in situ images,and spectra of 361 cotton canopies.To estimate cotton VWS values at the canopy scale,we developed two deep learning approaches that use in situ images and spectra,respectively.For the imagery-based method,given the high complexity of the in situ environment,we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes(CFS)dataset with over 1000 images for each scene-unit type.We performed pretrained convolutional neural networks(CNNs)training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy.The results showed that the Dark Net-19 model achieved satisfactory performance in CFS classification and VWS values estimation(R^(2)=0.91,root-mean-square error(RMSE)=6.35%).For the spectroscopy-based method,we first designed a one-dimensional regression network(1D CNN)with four convolutional layers.After dimensionality reduction by sensitive-band selection and principal component analysis,we fitted the 1D CNN with varying numbers of principal components(PCs).The 1D CNN model with the top 20 PCs performed best(R^(2)=0.93,RMSE=5.77%).These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.展开更多
Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen...Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination(UVE). Vegetation indices(VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares(PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy.UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models(UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index(NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients(r > 0.88). PLS regression models showed the lowest root mean square error(RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models.UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.展开更多
文章针对物流企业的订单分批问题,提出了改进的Canopy-k-means算法。该算法是采用Canopy算法依据最大最小原则生成初始聚类中心,并使用k-means聚类算法对其进行优化获取分批结果的。此外,文章针对不同规模的订单数据集,比较了该算法和...文章针对物流企业的订单分批问题,提出了改进的Canopy-k-means算法。该算法是采用Canopy算法依据最大最小原则生成初始聚类中心,并使用k-means聚类算法对其进行优化获取分批结果的。此外,文章针对不同规模的订单数据集,比较了该算法和先来先服务(first come first served,FCFS)、k-means以及Canopy-k-means算法的实际效果,实验结果表明:该算法可以避免k-means算法中k值选取的盲目性,同时可以有效地提高分拣效率以及降低分拣批次。展开更多
针对SMOTE算法和随机森林可较好解决不平衡数据集的分类问题但对少数类样本分类效果还有待提高的问题,融合Canopy和K-means两种聚类算法,设计了C-K-SMOTE改进算法。先后利用Canopy算法进行快速近似聚类,再利用K-means算法进行精准聚类,...针对SMOTE算法和随机森林可较好解决不平衡数据集的分类问题但对少数类样本分类效果还有待提高的问题,融合Canopy和K-means两种聚类算法,设计了C-K-SMOTE改进算法。先后利用Canopy算法进行快速近似聚类,再利用K-means算法进行精准聚类,得到精准聚类簇,最后利用SMOTE算法增加少数类样本数量,使数据趋于平衡。选取公开数据集KEEL(knowledge extraction on evolutionary learning)数据库中的不平衡数据集,结合随机森林分类模型进行了实验验证,实验表明C-K-SMOTE算法可有效平衡不平衡数据集。展开更多
Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exp...Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single operation.The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low cost.Results:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to landing.The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve estimates.Conclusions:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective.Thus,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest inventory.Future studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.展开更多
Reducing the threats of sulfate ion(SO42-)deposition to terrestrial ecosystems is a great challenge.The canopy interception effect on SO42-deposition has been well documented,but the interception efficiency of the gap...Reducing the threats of sulfate ion(SO42-)deposition to terrestrial ecosystems is a great challenge.The canopy interception effect on SO42-deposition has been well documented,but the interception efficiency of the gap edge remains unknown.Therefore,a subalpine dragon spruce(Picea asperata)plantation was evaluated in the upper reaches of the Yangtze River.The dynamics of the SO42-concentration in the throughfall were investigated from the gap edge to the closed canopy during the rainfall and snowfall periods from August 2015 to July 2016.The annual input of SO42-totaled 2.56 kg/ha through rainfall and 0.69 kg/ha through snowfall.The total annual net interception fluxes(NIFs)of SO42-at the gap edge and in the closed canopy were 1.48 kg/ha and 0.66 kg/ha,respectively,and the net interception ratios(NIRs)accounted for 45.40%and 20.25%,respectively.The NIF and the NIR of SO42-at the gap edge were higher than those in the closed canopy.Therefore,the results suggested that a significant amount of SO42-deposition was intercepted by the tree canopy in the subalpine plantation,with more SO42-deposition at the gap edge than in the closed canopy,which is beneficial for improving the water quality in the upper reaches of the Yangtze River via forest management.展开更多
基金supported by the Lorraine University of Excellence via the DEEPSURF project(ANR 70315-IDEX-04-LUE)。
文摘The increase in the frequency and intensity of drought events expected in the coming decades in Western Europe may disturb forest biogeochemical cycles and create nutrient deficiencies in trees.One possible origin of nutrient deficiency is the disturbance of the partitioning of the green leaf pool during the leaf senescence period between resorption,foliar leaching and senesced leaves.However,the effects of drought events on this partitioning and the consequences for the maintenance of tree nutrition are poorly documented.An experiment in a beech forest in Meuse(France)was conducted to assess the effect of drought events on nutrient canopy exchanges and on the partitioning of the green leaf pool during the leaf senescence period.The aim was to identify potential nutritional consequences of droughts for trees.Monitoring nutrient dynamics,including resorption,chemistry of green and senesced leaves,foliar absorption and leaching in mature beech stands from 2012 to 2019 allowed us to compare the nutrient exchanges for three nondry and three dry years(i.e.,with an intense drought event during the growing season).During dry years,we observed a decrease by almost a third of the potassium(K)partitioning to resorption(i.e.resorption efficiency),thus reducing the K reserve in trees for the next growing season.This result suggests that with the increased drought frequency and intensity expected for the coming decades,there will be a risk of potassium deficiency in trees,as already observed in a rainfall exclusion experiment on the same study site.Reduced foliar leaching and higher parititioning to the senesced leaves for K and phosphorus(P)were also observed.In addition,a slight increase in nitrogen(N)resorption efficiency occurred during dry years which is more likely to improve tree nutrition.The calcium(Ca)negative resorption decreased,with no apparent consequence in our study site.Our results show that nutrient exchanges in the canopy and the partitioning of the green leaf pool can be modified by drought events,and may have consequences on tree nutrition.
基金supported by the National Key Research and Development Program of China(2022YFD1500404)the National Natural Science Foundation of China(31801310)+1 种基金the Natural Science Projects of Universities in Jiangsu Province,China(21KJA210001)a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),China。
文摘Canopy temperature strongly influences crop yield formation and is closely related to plant physiological traits.However, the effects of nitrogen treatment on canopy temperature and rice growth have yet to be comprehensively examined. We conducted a two-year field experiment with three rice varieties(HD-5, NJ-9108, and YJ-805) and three nitrogen treatments(zero-N control(CK), 200 kg ha~(–1)(MN), and 300 kg ha~(–1)(HN)). We measured canopy temperature using a drone equipped with a high-precision camera at the six stages of the growth period. Generally,canopy temperature was significantly higher for CK than for MN and HN during the tillering, jointing, booting, and heading stages. The temperature was not significantly different among the nitrogen treatments between the milky and waxy stages. The canopy temperature of different rice varieties was found to follow the order: HD-5>NJ-9108>YJ-805, but the difference was not significant. The canopy temperature of rice was mainly related to plant traits, such as shoot fresh weight(correlation coefficient r=–0.895), plant water content(–0.912), net photosynthesis(–0.84), stomatal conductance(–0.91), transpiration rate(–0.90), and leaf stomatal area(–0.83). A structural equation model(SEM) showed that nitrogen fertilizer was an important factor affecting the rice canopy temperature.Our study revealed:(1) A suite of plant traits was associated with the nitrogen effects on canopy temperature,(2) the heading stage was the best time to observe rice canopy temperature, and(3) at that stage, canopy temperature was negatively correlated with rice yield, panicle number, and grain number per panicle. This study suggests that canopy temperature can be a convenient and accurate indicator of rice growth and yield prediction.
基金funded by the Key Research and Development Program of Shaanxi Province of China(2022NY-063)the Chinese Universities Scientific Fund(2452020018).
文摘The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.
基金supported by the USDA-ARS through CRIS project 6606-21220-014–00Dthe National Institute of Food and Agriculture–Specialty Crops Research Initiative grant 2016-51181-25408“Coordinated development of genetic tools for pecan”。
文摘A collection representing the native range of pecan was planted at the US DA-ARS Southeastern Fruit and Tree Nut Research Station,Byron,GA.The collection(867 trees)is a valuable genetic resource for characterizing important horticultural traits.Canopy density during leaf fall is important as the seasonal canopy dynamics provides insights to environmental cues and breeding potential of germplasm.The ability of visual raters to estimate canopy density on a subset of the provenance collection(76 trees)as an indicator of leaf shed during autumn along with image analysis values was explored.Mean canopy density using the image analysis software was less compared to visual estimates(11.9%vs 18.4%,respectively).At higher canopy densities,the raters overestimated foliage density,but overall agreement between raters and measured values was good(ρc=0.849 to 0.915),and inter-rater reliability was high(R^(2)=0.910 to 0.953).The provenance from Missouri(MOL),the northernmost provenance,had the lowest canopy density in November,and results show that the higher the latitude of the provenance,the lower the canopy density.Based on regression,the source provenance latitude explained 0.609 of the variation using image analysis,and0.551 to 0.640 when based on the rater estimates of canopy density.Visual assessment of pecan canopy density due to late season leaf fall for comparing pecan genotypes provides accurate and reliable estimates and could be used in future studies of the whole provenance collection.
基金supported by the State Key Laboratory of Cotton Biology Open Fund,China(CB2021A18)the Youth Scientific Research Foundation of Shandong Academy of Agricultural Sciences,China(2016YQN09)+1 种基金the Improved Variety Project of Shandong Province,China(2020LZGC002)the China Agriculture Research System of MOF and MARA(CARS-15-05).
文摘One of the most important objectives for breeders is to develop high-yield cultivars.The increase in crop yield has met with bottlenecks after the first green revolution,and more recent efforts have been focusing on achieving high photosynthetic efficiency traits in order to enhance the yield.Leaf shape is a significant agronomic trait of upland cotton that affects plant and canopy architecture,yield,and other production attributes.The major leaf shape types,including normal,sub-okra,okra,and super-okra,with varying levels of lobe severity,are controlled by a multiple allelic series of the D-genome locus L-D_(1).To analyze the effects of L-D_(1)alleles on leaf morphology,photosynthetic related traits and yield of cotton,two sets of near isogenic lines(NILs)with different alleles were constructed in Lumianyan 22(LMY22)and Lumianyan 28(LMY28)backgrounds.The analysis of morphological parameters and the results of virus-induced gene silencing(VIGS)showed that the regulation of leaf shape by L-D_(1)alleles was similar to a gene-dosage effect.Compared with the normal leaf,deeper lobes of the sub-okra leaf improved plant canopy structure by decreasing the leaf area index(LAI)and increasing the light transmittance rate(LTR),and the mid-range LAI of sub-okra leaf also guaranteed the accumulation of cotton biomass.Although the chlorophyll content(SPAD)of sub-okra leaf was lower than those of the other two leaf shapes,the net photosynthetic rate(Pn)of sub-okra leaf was higher than those of okra leaf and normal leaf at most stages.Thus,the improvements in canopy structure,as well as photosynthetic and physiological characteristics,contributed to optimizing the light environment,thereby increasing the total biomass and yield in the lines with a sub-okra leaf shape.Our results suggest that the sub-okra leaf may have practical application in cultivating varieties,and could enhance sustainable and profitable cotton production.
基金supported by the National Natural Science Foundation of China(No. 32101280)the Natural Science Foundation of Shanghai(No. 21ZR1420900)the Key R&D Project of Zhejiang(No. 2023C03138)
文摘Tree species diversity is vital for maintaining ecosystem functions,yet our ability to map the distribution of tree diversity is limited due to difficulties in traditional field-based approaches.Recent developments in spaceborne remote sensing provide unprecedented opportunities to map and monitor tree diversity more efficiently.Here we built partial least squares regression models using the multispectral surface reflectance acquired by Sentinel-2 satellites and the inventory data from 74 subtropical forest plots to predict canopy tree diversity in a national natural reserve in eastern China.In particular,we evaluated the underappreciated roles of the practical definition of forest canopy and phenological variation in predicting tree diversity by testing three different definitions of canopy trees and comparing models built using satellite imagery of different seasons.Our best models explained 42%–63%variations in observed diversities in cross-validation tests,with higher explanation power for diversity indices that are more sensitive to abundant species.The models built using imageries from early spring and late autumn showed consistently better fits than those built using data from other seasons,highlighting the significant role of transitional phenology in remotely sensing plant diversity.Our results suggested that the cumulative diameter(60%–80%)of the biggest trees is a better way to define the canopy layer than using the subjective fixeddiameter-threshold(5–12 cm)or the cumulative basal area(90%–95%)of the biggest trees.Remarkably,these approaches resulted in contrasting diversity maps that call attention to canopy structure in remote sensing of tree diversity.This study demonstrates the potential of mapping and monitoring tree diversity using the Sentinal-2 data in species-rich forests.
基金funded by Key Research Program of Frontier Sciences,CAS(ZDBS-LY-DQC012)the National Natural Science Foundation of China(41971321,41830108)+2 种基金XPCC Science and Technology Project(2022CB002-01)Open Fund of Key Laboratory of Oasis Eco-agriculture,XPCC(201801 and 202003)supported by Youth Innovation Promotion Association,CAS(Y2021047)。
文摘Verticillium wilt(VW)is a common soilborne disease of cotton.It occurs mainly in the seedling and bollopening stages and severely impairs the yield and quality of the fiber.Rapid and accurate identification and evaluation of VW severity(VWS)forms the basis of field cotton VW control,which has great significance to cotton production.Cotton VWS values are conventionally measured using in-field observations and laboratory test diagnoses,which require abundant time and professional expertise.Remote and proximal sensing using imagery and spectrometry have great potential for this purpose.In this study,we performed in situ investigations at three experimental sites in 2019 and 2021 and collected VWS values,in situ images,and spectra of 361 cotton canopies.To estimate cotton VWS values at the canopy scale,we developed two deep learning approaches that use in situ images and spectra,respectively.For the imagery-based method,given the high complexity of the in situ environment,we first transformed the task of healthy and diseased leaf recognition to the task of cotton field scene classification and then built a cotton field scenes(CFS)dataset with over 1000 images for each scene-unit type.We performed pretrained convolutional neural networks(CNNs)training and validation using the CFS dataset and then used the networks after training to classify scene units for each canopy.The results showed that the Dark Net-19 model achieved satisfactory performance in CFS classification and VWS values estimation(R^(2)=0.91,root-mean-square error(RMSE)=6.35%).For the spectroscopy-based method,we first designed a one-dimensional regression network(1D CNN)with four convolutional layers.After dimensionality reduction by sensitive-band selection and principal component analysis,we fitted the 1D CNN with varying numbers of principal components(PCs).The 1D CNN model with the top 20 PCs performed best(R^(2)=0.93,RMSE=5.77%).These deep learning-driven approaches offer the potential of assessing crop disease severity from spatial and spectral perspectives.
基金supported by the National Key Research and Development Program of China (2016YFD0300602)China Agricultural Research System (CARS-04-PS19)Chengdu Science and Technology Project (2020-YF09-00033-SN)。
文摘Assessing canopy nitrogen content(CNC) and canopy carbon content(CCC) of maize by hyperspectral remote sensing data permits estimating cropland productivity, protecting farmland ecology, and investigating the nitrogen and carbon cycles in the atmosphere. This study aimed to assess maize CNC and CCC using canopy hyperspectral information and uninformative variable elimination(UVE). Vegetation indices(VIs) and wavelet functions were adopted for estimating CNC and CCC under varying water and nitrogen regimes. Linear, nonlinear, and partial least squares(PLS) regression models were fitted to VIs and wavelet functions to estimate CNC and CCC, and were evaluated for their prediction accuracy.UVE was used to eliminate uninformative variables, improve the prediction accuracy of the models, and simplify the PLS regression models(UVE-PLS). For estimating CNC and CCC, the normalized difference vegetation index(NDVI, based on red edge and NIR wavebands) yielded the highest correlation coefficients(r > 0.88). PLS regression models showed the lowest root mean square error(RMSE) among all models. However, PLS regression models required nine VIs and four wavelet functions, increasing their complexity. UVE was used to retain valid spectral parameters and optimize the PLS regression models.UVE-PLS regression models improved validation accuracy and resulted in more accurate CNC and CCC than the PLS regression models. Thus, canopy spectral reflectance integrated with UVE-PLS can accurately reflect maize leaf nitrogen and carbon status.
文摘文章针对物流企业的订单分批问题,提出了改进的Canopy-k-means算法。该算法是采用Canopy算法依据最大最小原则生成初始聚类中心,并使用k-means聚类算法对其进行优化获取分批结果的。此外,文章针对不同规模的订单数据集,比较了该算法和先来先服务(first come first served,FCFS)、k-means以及Canopy-k-means算法的实际效果,实验结果表明:该算法可以避免k-means算法中k值选取的盲目性,同时可以有效地提高分拣效率以及降低分拣批次。
文摘针对SMOTE算法和随机森林可较好解决不平衡数据集的分类问题但对少数类样本分类效果还有待提高的问题,融合Canopy和K-means两种聚类算法,设计了C-K-SMOTE改进算法。先后利用Canopy算法进行快速近似聚类,再利用K-means算法进行精准聚类,得到精准聚类簇,最后利用SMOTE算法增加少数类样本数量,使数据趋于平衡。选取公开数据集KEEL(knowledge extraction on evolutionary learning)数据库中的不平衡数据集,结合随机森林分类模型进行了实验验证,实验表明C-K-SMOTE算法可有效平衡不平衡数据集。
基金supported in part by the Strategic Research Council at the Academy of Finland project“Competence Based Growth Through Integrated Disruptive Technologies of 3D Digitalization,Robotics,Geospatial Information and Image Processing/Computing-Point Cloud Ecosystem(293389,314312),Academy of Finland projects“Estimating Forest Resources and Quality-related Attributes Using Automated Methods and Technologies”(334830,334829)”,“Monitoring and understanding forest ecosystem cycles”(334060)。
文摘Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single operation.The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low cost.Results:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to landing.The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve estimates.Conclusions:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective.Thus,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest inventory.Future studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.
基金funded by the National Key R&D Program of China (Grant No. 2017YFC0503906)the Nation Nature Science Foundation of China (Grant Nos. 31570445, 31622018, 31670526 and 31901295)the Sichuan Province and Technology Project for Youth Innovation Team (Grant No. 2017TD0022)
文摘Reducing the threats of sulfate ion(SO42-)deposition to terrestrial ecosystems is a great challenge.The canopy interception effect on SO42-deposition has been well documented,but the interception efficiency of the gap edge remains unknown.Therefore,a subalpine dragon spruce(Picea asperata)plantation was evaluated in the upper reaches of the Yangtze River.The dynamics of the SO42-concentration in the throughfall were investigated from the gap edge to the closed canopy during the rainfall and snowfall periods from August 2015 to July 2016.The annual input of SO42-totaled 2.56 kg/ha through rainfall and 0.69 kg/ha through snowfall.The total annual net interception fluxes(NIFs)of SO42-at the gap edge and in the closed canopy were 1.48 kg/ha and 0.66 kg/ha,respectively,and the net interception ratios(NIRs)accounted for 45.40%and 20.25%,respectively.The NIF and the NIR of SO42-at the gap edge were higher than those in the closed canopy.Therefore,the results suggested that a significant amount of SO42-deposition was intercepted by the tree canopy in the subalpine plantation,with more SO42-deposition at the gap edge than in the closed canopy,which is beneficial for improving the water quality in the upper reaches of the Yangtze River via forest management.
