Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/36954
標題: 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
作者: 楊松勳
Yang, Sung-Hsun
關鍵字: canopy reflectance;植冠;remote sensing;predicting;yield;rice quality;反射光譜;遙測;預測;產量;米質
出版社: 農藝學系所
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摘要: 
近年來國人對食物品質的要求逐漸嚴苛,農民也改變以往只追求高產量的觀念,轉而希望栽種出高品質作物。藉著遙感探測提供大範圍且快速的觀測,栽種者將能在作物生長的關鍵期針對肥料缺乏或是遭受病蟲害的田區進行補救,以維持作物的品質及產量。
一般以遙測數據預測作物品質及產量的方法大致上有三種:第一種取植生指數(vegetation index, VI)配合線性或其他迴歸模式預測農藝性狀,第二種係由多個不同波長之反射率組成複迴歸模式(multiple linear regression, MLR),第三種則是淨最小平方法(partial least squares, PLS)。本研究則嘗試使用上述三種方法預測水稻台稉11號植冠反射光譜與米質及產量,並比較此三種方法的預測能力。
在預測台稉11號米質與產量的表現上,MLR預測直鏈澱粉含量(r=0.9964, RMSECV=0.0747)、粗蛋白(r=0.9999, RMSECV =0.0046)、產量(r=0.9999, RMSECV=0.0157)時的表現較佳。但預測凝膠展延性時以植生指數PSR(pigment specific ratio)配合二次函數預測模式能夠達到最佳的預測結果(r=0.877, RMSECV= 0.944)。因此依據本研究分析結果,由作物植冠反射率建立複迴歸模式應用預測米質性狀與產量時能得到不錯的預測結果。

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.
URI: http://hdl.handle.net/11455/36954
其他識別: U0005-2307200917204100
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