Samples of 12 hard winter wheats and their flours that produced breads varying in crumb grain scores were studied for 38 quality parameters including: wheat physical and chemical characteristics; flour ash and protein...Samples of 12 hard winter wheats and their flours that produced breads varying in crumb grain scores were studied for 38 quality parameters including: wheat physical and chemical characteristics; flour ash and protein contents,starch damage,swelling power,pasting characteristics,and flour particle size distribution; dough properties determined by a mixograph; and breadmaking properties for pup loaves (100g flour). Only two parameters,the protein content of wheat and the granulation of flour,showed significant correlations with bread crumb grain scores. Protein content of wheat ranging 12.9%～14.5% determined by an NIR method showed a weak inverse relationship (r =-0.61,p<0.05) with bread crumb grain score. Flour particle size distribution measured by both Alpine Air Jet Sieve and NIR methods revealed that the weight wt % of particles less than 38μm in size and representing 9.6%～19.3% of the flour weights was correlated positively (r =0 .78,p<0.01) with crumb grain score,whereas wt % of flour particles larger than 125μm had an inverse relationship (r =-0.60,p<0.05) with crumb grain score.展开更多
株高和叶面积指数(Leaf Area Index,LAI)反映着作物的生长发育状况。为了探究基于无人机可见光遥感提取冬小麦株高的可靠性,以及利用株高和可见光植被指数估算LAI的精度,本文获取了拔节期、抽穗期、灌浆期的无人机影像,提取了冬小麦株...株高和叶面积指数(Leaf Area Index,LAI)反映着作物的生长发育状况。为了探究基于无人机可见光遥感提取冬小麦株高的可靠性,以及利用株高和可见光植被指数估算LAI的精度,本文获取了拔节期、抽穗期、灌浆期的无人机影像,提取了冬小麦株高与可见光植被指数,使用逐步回归、偏最小二乘、随机森林、人工神经网络四种方法建立LAI估测模型,并对株高提取及LAI估测情况进行精度评价。结果显示:(1)株高提取值Hc与实测值Hd高度拟合(R^(2)=0.894,RMSE=6.695,NRMSE=9.63%),株高提取效果好;(2)与仅用可见光植被指数相比,基于株高与可见光植被指数构建的LAI估测模型精度更高,且随机森林为最优建模方法,当其决策树个数为50时模型估测效果最好(R^(2)=0.809,RMSE=0.497,NRMSE=13.85%,RPD=2.336)。利用无人机可见光遥感方法,高效、准确、无损地实现冬小麦株高及LAI提取估测可行性较高,该研究结果可为农情遥感监测提供参考。展开更多
文摘Samples of 12 hard winter wheats and their flours that produced breads varying in crumb grain scores were studied for 38 quality parameters including: wheat physical and chemical characteristics; flour ash and protein contents,starch damage,swelling power,pasting characteristics,and flour particle size distribution; dough properties determined by a mixograph; and breadmaking properties for pup loaves (100g flour). Only two parameters,the protein content of wheat and the granulation of flour,showed significant correlations with bread crumb grain scores. Protein content of wheat ranging 12.9%～14.5% determined by an NIR method showed a weak inverse relationship (r =-0.61,p<0.05) with bread crumb grain score. Flour particle size distribution measured by both Alpine Air Jet Sieve and NIR methods revealed that the weight wt % of particles less than 38μm in size and representing 9.6%～19.3% of the flour weights was correlated positively (r =0 .78,p<0.01) with crumb grain score,whereas wt % of flour particles larger than 125μm had an inverse relationship (r =-0.60,p<0.05) with crumb grain score.
文摘株高和叶面积指数(Leaf Area Index,LAI)反映着作物的生长发育状况。为了探究基于无人机可见光遥感提取冬小麦株高的可靠性,以及利用株高和可见光植被指数估算LAI的精度,本文获取了拔节期、抽穗期、灌浆期的无人机影像,提取了冬小麦株高与可见光植被指数,使用逐步回归、偏最小二乘、随机森林、人工神经网络四种方法建立LAI估测模型,并对株高提取及LAI估测情况进行精度评价。结果显示:(1)株高提取值Hc与实测值Hd高度拟合(R^(2)=0.894,RMSE=6.695,NRMSE=9.63%),株高提取效果好;(2)与仅用可见光植被指数相比,基于株高与可见光植被指数构建的LAI估测模型精度更高,且随机森林为最优建模方法,当其决策树个数为50时模型估测效果最好(R^(2)=0.809,RMSE=0.497,NRMSE=13.85%,RPD=2.336)。利用无人机可见光遥感方法,高效、准确、无损地实现冬小麦株高及LAI提取估测可行性较高,该研究结果可为农情遥感监测提供参考。
