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亚洲精细农业与计算机农业应用联合大会

作者： Wang Dacheng 1 2 Wang Jihua 2 Li Cunjun 2 Zhang Dongyan 1 2 Huang Jingfeng 1 1 Institute of Agricultural Remote Sensing and Information Technology Zhejiang University 310029 China 2 National Engineering Research Center for Information Technology in Agriculture Beijing 100097 China

Canopy leaf chlorophyll density is a key index for evaluating crop potential photosynthetic efficiency and nutritional stress. Leaf chlorophyll density estimate using canopy hyperspectral vegetation indices provides a rapid and non-destructive method to evaluate yield predictions. A systematic comparison of two approaches to estimate chlorophyll density using 6 spectral vegetation indices （VIs） was presented in this study. In this study ,the traditional statistical method based on power regression analyses was compared to the emerging computationally powerful techniques based on artificial neural network （ANN）. The regression models of TCARI 、SAVI 、MSAVI and RDVIgreen were found to be more suitable for predicting chlorophyll density when only traditional statistical method was used,especially TCARI and RDVI. ANN method was more appropriate to develop prediction models. The comparisons between these two methods were based on analysis of the statistic parameters. Results obtained using Root Mean Square Error （RMSE） for ANNs were significantly lower than the traditional method. Especially for TCARI,T values performed 57.32% ,successed in using Back-Propagation Network （BPN）. From this analysis,it is concluded that the neural network is more robust to train and estimate crop chlorophyll density from remote sensing data.

关键词： wheat hyperspectral vegetation index chlorophyll density artificial neural network mathematical power regression

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Error Analysis in Hyperspectral Remote Sensing of chlorophyll density for Inland Water Environment

Error Analysis in Hyperspectral Remote Sensing of Chlorophyl...

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The 12th Asian Pacific Confederation of Chemical Engineering Congress

作者： Abdoul Nasser Ibrahim School of Resources and Environmental Engineering East China University of Science and TechnologyShanghai 200237China

＜正＞The application of remote sensors to inland waterbodies for indirect measurement of chloro- phyn density is often a daunting *** these"optically complex"water environments,a strategy for keeping the measurement ... 详细信息

关键词： hyperspectral remote sensing chlorophyll density phytoplankton reflectance probability density

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Effects of dense planting patterns on photosynthetic traits of different vertical layers and yield of wheat under different nitrogen rates

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The Crop Journal 2024年第2期12卷 594-604页

作者： Cuicun Wang Ke Zhang Qing Liu Xiufeng Zhang Zhikuan Shi Xue Wang Caili Guo Qiang Cao Yongchao Tian Yan Zhu Xiaojun Liu Weixing Cao National Engineering and Technology Center for Information Agriculture Nanjing Agricultural UniversityNanjing 210095JiangsuChina MOE Engineering Research Center of Smart Agricultural Nanjing Agricultural UniversityNanjing 210095JiangsuChina MARA Key Laboratory for Crop System Analysis and Decision Making Nanjing Agricultural UniversityNanjing 210095JiangsuChina Jiangsu Key Laboratory for Information Agriculture Nanjing Agricultural UniversityNanjing 210095JiangsuChina Institute of Smart Agriculture Nanjing Agricultural UniversityNanjing 210095JiangsuChina

A two-year field experiment was conducted to measure the effects of densification methods on photosynthesis and yield of densely planted ***-plant and inter-row distances were used to define ratefixed pattern(RR)and row-fixed pattern(RS)density ***,four nitrogen(N)rates(0,144,192,and 240 kg N ha-1,termed N0,N144,N192,and N240)were applied with three densities(225,292.5,and 360×10^(4)plants ha^(-1),termed D225,D292.5,and D360).The wheat canopy was clipped into three equal vertical layers(top,middle,and bottom layers),and their chlorophyll density(Ch D)and photosynthetically active radiation interception(FIPAR)were *** showed that the response of Ch D and FIPAR to N rate,density,and pattern varied with different layers.N rate,density,and pattern had significant interaction effects on Ch *** maximum values of whole-canopy Ch D in the two seasons appeared in N240 combined with D292.5 and D360 under RR,*** two growing seasons,FIPAR values of RR were higher than those of RS by 29.37%for the top layer and 5.68%for the middle layer,while lower than those of RS by 20.62%for the bottom layer on *** a low N supply(N0),grain yield was not significantly affected by density for both *** N240,increasing density significantly increased yield under RR,but D360 of RS significantly decreased yield by 3.72%and 9.00%versus D225 in two seasons,*** an appropriate and sufficient N application,RR increased the yield of densely planted wheat more than ***,the maximum yield in two seasons appeared in the combination of D360 with N144 or N192 rather than of D225 with N240 under both patterns,suggesting that dense planting combined with an appropriate N-reduction application is feasible to increase photosynthesis capacity and yield.

关键词： chlorophyll density Densification method Nitrogen Photosynthetically active radiation interception Wheat

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