Estimation of Winter Wheat Tiller Number Based on Optimization of Gradient Vegetation Characteristics

被引:7
|
作者
Wu, Fei [1 ,2 ]
Wang, Junchan [3 ]
Zhou, Yuzhuang [1 ,2 ]
Song, Xiaoxin [1 ,2 ]
Ju, Chengxin [1 ,2 ]
Sun, Chengming [1 ,2 ]
Liu, Tao [1 ,2 ]
机构
[1] Yangzhou Univ, Agr Coll, Jiangsu Key Lab Crop Genet & Physiol, Jiangsu Key Lab Crop Cultivat & Physiol, Yangzhou 225009, Jiangsu, Peoples R China
[2] Yangzhou Univ, Jiangsu Coinnovat Ctr Modern Prod Technol Grain C, Yangzhou 225009, Jiangsu, Peoples R China
[3] Minist Agr & Rural Affairs, Key Lab Wheat Biol & Genet Improvement Low & Midd, Lixiahe Inst Agr Sci Jiangsu, Yangzhou 225012, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
winter wheat; tiller number; vegetation index; gradient feature; regression models; LEAF-AREA INDEX; SPECTRAL REFLECTANCE; DENSITY; YIELD; RED;
D O I
10.3390/rs14061338
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Tiller are an important biological characteristic of wheat, a primary food crop. Accurate estimation of tiller number can help monitor wheat growth and is important in forecasting wheat yield. However, because of leaf cover and other factors, it is difficult to estimate tiller number and the accuracy of estimates based on vegetation indices is low. In this study, a gradual change feature was introduced to optimize traditional prediction models of wheat tiller number. Accuracy improved in optimized models, and model R2 values for three varieties of winter wheat were 0.7044, 0.7060, and 0.7357. The optimized models improved predictions of tiller number in whole wheat fields. Thus, compared with the traditional linear model, the addition of a gradual change feature greatly improved the accuracy of model predictions of wheat tiller number.
引用
收藏
页数:17
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