Study of hyperspectral detection for nitrogen content of apple leaves
AN Jing1, YAO Guoqing1, ZHU Xicun2
1. School of Information Engineering, China University of Geosciences(Beijing), Beijing 100083, China;
2. College of Resources and Environment, Shandong Agricultural University, Tai'an 271018, China
Nitrogen(N)content of apple leaves is an important indicator for estimating growth status of apple tree. Quantitative inversion of the nitrogen content of apple leaves using high spectral technology can provide the theoretical basis for information management of apple tree. In this paper, the hyperspectral reflectance and nitrogen content of apple leaf samples were measured by using ASD FieldSpec 3 spectrometer. The authors constructed multiple regression analysis of the relationships between nitrogen content of apple tree leaves and the original spectrum, the first-order derivative and the transformation forms, selected four wavebands which are more sensitive to the nitrogen change, and constructed the retrieval model for nitrogen content of apple leaves using back propagation (BP) artificial neural network (ANN) algorithm. Finally, the model was optimized and tested. The results show that the model is an effective means to improve capability of predicting apple tree nitrogen content based on BP artificial neural network algorithm.
安静, 姚国清, 朱西存. 苹果叶片氮素含量高光谱检测研究[J]. 国土资源遥感, 2016, 28(2): 67-71.
AN Jing, YAO Guoqing, ZHU Xicun. Study of hyperspectral detection for nitrogen content of apple leaves. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 67-71.
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2016, Vol. 28 
