It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications.The winter wheat diseases,in combination with nitrog...It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications.The winter wheat diseases,in combination with nitrogen-water stress,are therefore common causes of yield loss in winter wheat in China.Powdery mildew(Blumeria graminis)and stripe rust(Puccinia striiformis f.sp.Tritici)are two of the most prevalent winter wheat diseases in China.This study investigated the potential of continuous wavelet analysis to identify the powdery mildew,stripe rust and nitrogen-water stress using canopy hyperspectral data.The spectral normalization process was applied prior to the analysis.Independent t-tests were used to determine the sensitivity of the spectral bands and vegetation index.In order to reduce the number of wavelet regions,correlation analysis and the independent t-test were used in conjunction to select the features of greatest importance.Based on the selected spectral bands,vegetation indices and wavelet features,the discriminate models were established using Fisher’s linear discrimination analysis(FLDA)and support vector machine(SVM).The results indicated that wavelet features were superior to spectral bands and vegetation indices in classifying different stresses,with overall accuracies of 0.91,0.72,and 0.72 respectively for powdery mildew,stripe rust and nitrogen-water by using FLDA,and 0.79,0.67 and 0.65 respectively by using SVM.FLDA was more suitable for differentiating stresses in winter wheat,with respective accuracies of 78.1%,95.6%and 95.7%for powdery mildew,stripe rust,and nitrogen-water stress.Further analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for identification.The accuracies of high-scale and low-scale features with an overall accuracy(OA)of 0.61 and 0.73 respectively were lower than those of all wavelet features with an OA of 0.88.The detection of the severity of stripe rust using this method showed an enhanced reliability(R^(2)=0.828).展开更多
针对河西绿洲灌区水资源短缺、玉米田氮肥施用量高等生产生态问题,在节水减氮条件下,分析增加种植密度补偿水氮减量导致玉米减产的效应,为水氮节约型玉米高效生产提供理论依据与技术支撑。基于2016年布设的裂裂区田间试验,主区为2种灌...针对河西绿洲灌区水资源短缺、玉米田氮肥施用量高等生产生态问题,在节水减氮条件下,分析增加种植密度补偿水氮减量导致玉米减产的效应,为水氮节约型玉米高效生产提供理论依据与技术支撑。基于2016年布设的裂裂区田间试验,主区为2种灌水定额:灌水减量20%(W1,3240 m^(3) hm^(–2))和传统灌水(W2,4050 m^(3) hm^(–2)),裂区为2种施氮量:减量施氮25%(N1,270 kg hm^(–2))和传统施氮(N2,360 kg hm^(–2)),裂裂区为3种玉米密度:传统种植密度(D1,7.50万株hm^(–2))、增密30%(D2,9.75万株hm^(–2))和增密60%(D3,12.00万株hm^(–2)),通过测定2020—2021年玉米籽粒产量和生物产量,分析干物质积累及其分配、转运特征,量化产量构成因素,明确增密对水氮减量玉米产量的补偿效应及机制。研究表明,减水、减氮降低了玉米籽粒产量和生物产量,而增密30%能够补偿因水氮同步减量造成的产量负效应,且维持较高的施氮量有利于玉米增产节水。W1N1D1(减量灌水减量施氮及传统密度)较W2N2D1(对照:传统灌水传统施氮及传统密度)籽粒产量和生物产量分别降低9.1%~15.0%与10.0%~11.0%,但W1N1D2(减量灌水减量施氮及增密30%)与W2N2D1差异不显著。W1N2D2(减量灌水传统施氮及增密30%)较W2N2D1籽粒和生物产量分别提高12.9%~15.4%与6.4%~12.0%。增密30%能够补偿水氮同步减量造成玉米减产的主要原因是W1N1D2能增加玉米穗数,进而提高玉米灌浆初期至成熟期干物质积累量和苗期到大喇叭口期群体生长速率及花前转运率。增密30%在灌水减量和传统施氮条件下促进玉米增产的主要原因是W1N2D2可增加玉米穗数,提高玉米生育期干物质积累量与群体生长速率,促进穗部干物质分配,提高花前转运量、转运率及转运贡献率。因此,增密30%是绿洲灌区水氮同步减量玉米稳产高产的可行措施,是氮肥不减但减水20%玉米节水增产有效举措。展开更多
基金supported by Free Exploration Project of the State Key Laboratory of Remote Sensing Science at Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences(17ZY-01)the National Natural Science Foundation of China(61661136004)Hainan Provincial Department of Science and Technology under Grant(ZDKJ2016021).
文摘It is necessary to quantitatively identify different diseases and nitrogen-water stress for the guidance in spraying specific fungicides and fertilizer applications.The winter wheat diseases,in combination with nitrogen-water stress,are therefore common causes of yield loss in winter wheat in China.Powdery mildew(Blumeria graminis)and stripe rust(Puccinia striiformis f.sp.Tritici)are two of the most prevalent winter wheat diseases in China.This study investigated the potential of continuous wavelet analysis to identify the powdery mildew,stripe rust and nitrogen-water stress using canopy hyperspectral data.The spectral normalization process was applied prior to the analysis.Independent t-tests were used to determine the sensitivity of the spectral bands and vegetation index.In order to reduce the number of wavelet regions,correlation analysis and the independent t-test were used in conjunction to select the features of greatest importance.Based on the selected spectral bands,vegetation indices and wavelet features,the discriminate models were established using Fisher’s linear discrimination analysis(FLDA)and support vector machine(SVM).The results indicated that wavelet features were superior to spectral bands and vegetation indices in classifying different stresses,with overall accuracies of 0.91,0.72,and 0.72 respectively for powdery mildew,stripe rust and nitrogen-water by using FLDA,and 0.79,0.67 and 0.65 respectively by using SVM.FLDA was more suitable for differentiating stresses in winter wheat,with respective accuracies of 78.1%,95.6%and 95.7%for powdery mildew,stripe rust,and nitrogen-water stress.Further analysis was performed whereby the wavelet features were then split into high-scale and low-scale feature subsets for identification.The accuracies of high-scale and low-scale features with an overall accuracy(OA)of 0.61 and 0.73 respectively were lower than those of all wavelet features with an OA of 0.88.The detection of the severity of stripe rust using this method showed an enhanced reliability(R^(2)=0.828).
文摘针对河西绿洲灌区水资源短缺、玉米田氮肥施用量高等生产生态问题,在节水减氮条件下,分析增加种植密度补偿水氮减量导致玉米减产的效应,为水氮节约型玉米高效生产提供理论依据与技术支撑。基于2016年布设的裂裂区田间试验,主区为2种灌水定额:灌水减量20%(W1,3240 m^(3) hm^(–2))和传统灌水(W2,4050 m^(3) hm^(–2)),裂区为2种施氮量:减量施氮25%(N1,270 kg hm^(–2))和传统施氮(N2,360 kg hm^(–2)),裂裂区为3种玉米密度:传统种植密度(D1,7.50万株hm^(–2))、增密30%(D2,9.75万株hm^(–2))和增密60%(D3,12.00万株hm^(–2)),通过测定2020—2021年玉米籽粒产量和生物产量,分析干物质积累及其分配、转运特征,量化产量构成因素,明确增密对水氮减量玉米产量的补偿效应及机制。研究表明,减水、减氮降低了玉米籽粒产量和生物产量,而增密30%能够补偿因水氮同步减量造成的产量负效应,且维持较高的施氮量有利于玉米增产节水。W1N1D1(减量灌水减量施氮及传统密度)较W2N2D1(对照:传统灌水传统施氮及传统密度)籽粒产量和生物产量分别降低9.1%~15.0%与10.0%~11.0%,但W1N1D2(减量灌水减量施氮及增密30%)与W2N2D1差异不显著。W1N2D2(减量灌水传统施氮及增密30%)较W2N2D1籽粒和生物产量分别提高12.9%~15.4%与6.4%~12.0%。增密30%能够补偿水氮同步减量造成玉米减产的主要原因是W1N1D2能增加玉米穗数,进而提高玉米灌浆初期至成熟期干物质积累量和苗期到大喇叭口期群体生长速率及花前转运率。增密30%在灌水减量和传统施氮条件下促进玉米增产的主要原因是W1N2D2可增加玉米穗数,提高玉米生育期干物质积累量与群体生长速率,促进穗部干物质分配,提高花前转运量、转运率及转运贡献率。因此,增密30%是绿洲灌区水氮同步减量玉米稳产高产的可行措施,是氮肥不减但减水20%玉米节水增产有效举措。
