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Sui X.,Shandong Academy of Sciences | Zhang X.,Shandong Academy of Sciences | Wang Y.,Shandong Academy of Sciences | Li S.,Chinese Academy of Agricultural Sciences | Li S.,Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops
Applied Mechanics and Materials | Year: 2013

Leaf thickness is one important index of describing plant growing conditions. But the measurements now are all tedious and destructive. In order to carry out real-time, live, non-destructive testing of leaf thickness, the study took cotton leaves as the research object. The correlations of leaf thickness with reflectance, vegetable index and spectral figure index were analyzed separately. And then the cause of correlation was studied. Three regression models were set up with the 3 parameters which had high correlation with leaf thickness. Among these models, the highest relative error is 7.4%, and the RMSE is 0.051 mm. It is feasible to measure alive leaf thickness untouchably with Hyper spectrum. © (2013) Trans Tech Publications, Switzerland.


Jin X.-L.,Chinese Academy of Agricultural Sciences | Jin X.-L.,Beijing Research Center for Information Technology in Agriculture | Diao W.-Y.,Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops | Xiao C.-H.,Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops | And 6 more authors.
PLoS ONE | Year: 2013

Crop agronomic parameters (leaf area index (LAI), nitrogen (N) uptake, total chlorophyll (Chl) content) are very important for the prediction of crop growth. The objective of this experiment was to investigate whether the wheat LAI, N uptake, and total Chl content could be accurately predicted using spectral indices collected at different stages of wheat growth. Firstly, the product of the optimized soil-adjusted vegetation index and wheat biomass dry weight (OSAVI×BDW) were used to estimate LAI, N uptake, and total Chl content; secondly, BDW was replaced by spectral indices to establish new spectral indices (OSAVI×OSAVI, OSAVI×SIPI, OSAVI×CIred edge, OSAVI×CIgreen mode and OSAVI×EVI2); finally, we used the new spectral indices for estimating LAI, N uptake, and total Chl content. The results showed that the new spectral indices could be used to accurately estimate LAI, N uptake, and total Chl content. The highest R2 and the lowest RMSEs were 0.711 and 0.78 (OSAVI×EVI2), 0.785 and 3.98 g/m2 (OSAVI×CIred edge) and 0.846 and 0.65 g/m2 (OSAVI×CIred edge) for LAI, nitrogen uptake and total Chl content, respectively. The new spectral indices performed better than the OSAVI alone, and the problems of a lack of sensitivity at earlier growth stages and saturation at later growth stages, which are typically associated with the OSAVI, were improved. The overall results indicated that this new spectral indices provided the best approximation for the estimation of agronomic indices for all growth stages of wheat. © 2013 Jin et al.


Jin X.L.,Chinese Academy of Agricultural Sciences | Jin X.L.,Beijing Research Center for Information Technology in Agriculture | Diao W.Y.,Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops | Xiao C.H.,Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops | And 6 more authors.
Journal of Agricultural Science | Year: 2015

SUMMARY Crop nitrogen (N) status is an important indicator of crop health and predictor of subsequent crop yield. The present study was conducted to analyse the relationships between nitrogen nutrition index (NNI), nitrogen biomass difference (ΔNB) and spectral indices in wheat, and then attempt to improve field N management. Spectral indices and concurrent sample N and biomass parameters were obtained from the Shihezi University experimental site in Xinjiang, China during 2009 and 2010. The results showed that all spectral indices were significantly correlated with NNI. Regression functions with the highest determination coefficient (R 2) and the lowest root mean square error (RMSE) were used to improve prediction of NNI, and then the selected spectral index was used to estimate NNI and ΔNB. The strongest relationships were observed for the products of modified normalized difference 705 × biomass dry weight (BND705) and the enhanced vegetation index 2 (EVI2) for estimating NNI. There were also strong relationships between the NNI and the normalized NNI (ΔNNI) as well as between ΔNNI and ΔNB, with a linear relationship between ΔNB and the spectral index BND705 and a linear relationship between ΔNB and the spectral index EVI2. These results indicated that BND705 and EVI2 can be used to improve the accuracy of NNI estimation, and the correlations of ΔNB and NNI with BND705 and EVI2 can be used to further improve field N management in wheat. Copyright © Cambridge University Press 2014.


Jin X.,Chinese Academy of Agricultural Sciences | Jin X.,Yangzhou University | Diao W.,Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops | Xiao C.,Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops | And 6 more authors.
New Zealand Journal of Crop and Horticultural Science | Year: 2013

Leaf total chlorophyll content (LTCC) provides valuable information about the physiological status of crops. LTCC could potentially be rapidly and non-destructively estimated via remote sensing. The objective of this experiment was to develop precise agricultural practices for predicting the LTCC of wheat. In this study, we compared certain spectral indices using the determination coefficient (R 2), and then combined these indices using stepwise regression methods (SRM) or partial least squares (PLS). We obtained a new index that was more effective at predicting LTCC by SRM than the most effective individual indices were: 3.575Red edge Model-1.118PSSRb. Results showed that for LTCC = 3.575Red edge Model-1.118PSSRb, the R 2 value was 0.87, and the corresponding root mean square error (RMSE) was 0.38 g/m2. We used the PLS to estimate LTCC, and gained an R 2 value of 0.92 and RMSE of 0.31 g/m2. The results showed that PLS was better than SRM; these results indicated two methods could be used to improve the estimation accuracy of LTCC. © 2013 © 2013 The Royal Society of New Zealand.


Jin X.-L.,Chinese Academy of Agricultural Sciences | Jin X.-L.,Yangzhou University | Wang K.-R.,Chinese Academy of Agricultural Sciences | Wang K.-R.,Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops | And 6 more authors.
Field Crops Research | Year: 2012

Leaf total chlorophyll content (LTCC) is an important indicator for assessment of crop health and prediction of crop yield. The objective of this study was to develop a precise agricultural practice that could estimate wheat LTCC. In this study, we compared two methods of LTCC estimation: one method used the products of spectral parameters and biomass dry weight (BDW), and the other method used stepwise regression methods (SRM). We selected the highest determination coefficient (R 2) simulation model to improve prediction accuracy. The results showed that for the mND705×BDW index, the R 2 was 0.9639 and the root mean square error (RMSE) was 0.202g/m 2. For the 3.575Red edge model-1.118PSSRb index, the R 2 was 0.868 and RMSE was 0.384g/m 2. The mND705×BDW index accounted for 96.39% of LTCC, while the 3.575Red edge model-1.118PSSRb accounted for 86.8% of LTCC. Further, the RMSE of mND705×BDW was lower than that of 3.575Red edge model-1.118PSSRb for predicting LTCC. The results indicated that the spectral parameters×BDW methods, in which spectral parameters defection was improved, was superior to SRM. © 2012 Elsevier B.V.

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