In order to clarify the relationships between soil pH and flue-cured tobacco growth and dry matter accumulation, effects of soil pH on root morphology and vigor, aboveground agronomic traits and dry matter accumulatio...In order to clarify the relationships between soil pH and flue-cured tobacco growth and dry matter accumulation, effects of soil pH on root morphology and vigor, aboveground agronomic traits and dry matter accumulation of flue-cured tobacco were investigated by pot experiment. The results showed that on the whole, the intensity of soil pH on flue-cured tobacco growth and dry matter accumulation ranked as pH=6＇s 〉 pH=7＇s 〉 pH=5＇s 〉 pH=4＇s 〉 pH=8＇s. Acidic soil （pH=4） was not conducive to the early growth of tobacco plants, reduced root vigor and affected dry matter accumulation; and alkaline soil （pH=8） was not conducive to the growth of tobacco roots and shoot, reduced root vigor and affected dry matter accumulation. In conclusion, the suitable pH of soil for growth of flue-cured tobacco in Xiangxi is 5-7, but weakly acidic soil is the best.展开更多
Wheat biomass can be estimated using appropriate spectral vegetation indices.However,the accuracy of estimation should be further improved for on-farm crop management.Previous studies focused on developing vegetation ...Wheat biomass can be estimated using appropriate spectral vegetation indices.However,the accuracy of estimation should be further improved for on-farm crop management.Previous studies focused on developing vegetation indices,however limited research exists on modeling algorithms.The emerging Random Forest(RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression modeling.The objectives of this study were to(1) investigate the applicability of the RF regression algorithm for remotely estimating wheat biomass,(2) test the performance of the RF regression model,and(3) compare the performance of the RF algorithm with support vector regression(SVR) and artificial neural network(ANN) machine-learning algorithms for wheat biomass estimation.Single HJ-CCD images of wheat from test sites in Jiangsu province were obtained during the jointing,booting,and anthesis stages of growth.Fifteen vegetation indices were calculated based on these images.In-situ wheat above-ground dry biomass was measured during the HJ-CCD data acquisition.The results showed that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage,and its robustness is as good as SVR but better than ANN.The RF algorithm provides a useful exploratory and predictive tool for estimating wheat biomass on a large scale in Southern China.展开更多
基金Supported by College Students’Innovative Experiment Plan of Hunan Agricultural University(XCX16132)Project of Tobacco Monopoly Bureau of Hunan Province(xx15-18Aa01)~~
文摘In order to clarify the relationships between soil pH and flue-cured tobacco growth and dry matter accumulation, effects of soil pH on root morphology and vigor, aboveground agronomic traits and dry matter accumulation of flue-cured tobacco were investigated by pot experiment. The results showed that on the whole, the intensity of soil pH on flue-cured tobacco growth and dry matter accumulation ranked as pH=6＇s 〉 pH=7＇s 〉 pH=5＇s 〉 pH=4＇s 〉 pH=8＇s. Acidic soil （pH=4） was not conducive to the early growth of tobacco plants, reduced root vigor and affected dry matter accumulation; and alkaline soil （pH=8） was not conducive to the growth of tobacco roots and shoot, reduced root vigor and affected dry matter accumulation. In conclusion, the suitable pH of soil for growth of flue-cured tobacco in Xiangxi is 5-7, but weakly acidic soil is the best.
基金supported by the National Natural Science Foundation of China(No.31271642)the Natural Science Foundation of Education Department of Jiangsu Province(No.09KJB20013,No.12KJB520018)+1 种基金the Six Talent Summit Project of Jiangsu Province(No.2011-NY039)the Science and Technology Innovation Development Foundation of Yangzhou University(No.2015CXJ022)
文摘Wheat biomass can be estimated using appropriate spectral vegetation indices.However,the accuracy of estimation should be further improved for on-farm crop management.Previous studies focused on developing vegetation indices,however limited research exists on modeling algorithms.The emerging Random Forest(RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression modeling.The objectives of this study were to(1) investigate the applicability of the RF regression algorithm for remotely estimating wheat biomass,(2) test the performance of the RF regression model,and(3) compare the performance of the RF algorithm with support vector regression(SVR) and artificial neural network(ANN) machine-learning algorithms for wheat biomass estimation.Single HJ-CCD images of wheat from test sites in Jiangsu province were obtained during the jointing,booting,and anthesis stages of growth.Fifteen vegetation indices were calculated based on these images.In-situ wheat above-ground dry biomass was measured during the HJ-CCD data acquisition.The results showed that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage,and its robustness is as good as SVR but better than ANN.The RF algorithm provides a useful exploratory and predictive tool for estimating wheat biomass on a large scale in Southern China.
