利用植被反射量測預測水稻米質與產量特性

楊松勳

doi:10.6845/NCHU.2009.01010

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利用植被反射量測預測水稻米質與產量特性

Predicting rice quality characteristics and yield using canopy reflectance measurements

楊松勳 , Masters　　Advisor：郭寶錚　　

繁體中文 DOI： 10.6845/NCHU.2009.01010

植冠；反射光譜；遙測；預測；產量；米質； canopy reflectance ； remote sensing ； predicting ； yield ； rice quality

Abstract │ Reference (41) │ Altmetrics

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As the demand of the quality on food is getting rigid, customers have changed their target from high yield crop to high quality crop. Remote sensing technique can provide a fast and large-scale observation so that farmers can monitor the physiological status or disease infection in-time and use the information to determine fertilizer application and protection strategy.
In general, there are three different approaches to predict rice quality and yield when using canopy reflectance measurements. The first method employs vegetation index with suitable regression models. The second method incorporates several reflectance wavebands into a multiple linear regression (MLR) model. The third method is partial least squares regression model. The objectives of this study were to predict some rice quality characteristics and yield of rice variaty (TK11) using three different methods and compare their predictive ability. 
The results indicated that MLR model had the best predictive ability in predicting rice amylose content (r=0.9964, RMSECV=0.0747), crude protein content (r=0.9999, RMSECV=0.0046), and yield (r=0.9999, RMSECV =0.0157). Using vegetation index PSR with quadratic regression model to predict gel consistency is better than other methods(r=0.877, RMSECV =0.944). We conclude that these rice quality characteristics and yield can be predicted by MLR method with canopy reflectance measurements.

Reference ( 41 ) 〈TOP〉

劉長利，2006。不同前處理方法對台農67號(水稻)植被反射光譜預測產量之影響。國立中興大學農藝系碩士論文。
連結：
Aparicio, N., D. Villegas, J. Casadesus, J. L. Araus, and C. Royo. 2000. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J. 92 : 83-91.
連結：
Baret, F. and G. Guyot. 1991. Potentials and limits of vegetation indices for LAI and PAR assessments. Remote Sens. Environ. 35:161-173.
連結：
Blackmer, T. M., J. S. Schepers, G. E. Varvel., and E. A. Walter-Shea. 1996. Nitrogen deficiency detection using shortwave radiation from irrigated corn canopies. Agron. J. 88 : 1-5.
連結：
Buscaglia, H. J. and J. J. Varco. 2002. Early detection of cotton leaf nitrogen status using leaf reflectance. J. Plant Nutr. 25:2067-2080.
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