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当前位置: 首页 > 文章 > Field monitoring of wheat seedling stage with hyperspectral imaging 国际农业与生物工程学报 2016,9 (5)
Position: Home > Articles > Field monitoring of wheat seedling stage with hyperspectral imaging International Journal of Agricultural and Biological Engineering 2016,9 (5)

Field monitoring of wheat seedling stage with hyperspectral imaging

作  者：
Qiong Wu;Cheng Wang;Fang Jing-jing;Jianwei J
单  位：
College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 1008611, China;Beijing Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China;College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 1008611, Chin
关键词：
seedling stage;based on;hyperspectral imaging;wheat;leaves;imaging spectromete
摘  要：
Nutrient elements such as chlorophyll, nitrogen and water at the seedling stage are important key factors that could influence growth, development and even the final yield of wheat. In this study, the spectral data of canopy and single wheat plant leaves at seedling stage were acquired in field by using ASD non-imaging hyperspectrometer and imaging spectrometer respectively to establish prediction models for monitoring the growth at the seedling stage of wheat. According to the comparative analysis of models results built through partial least square algorithm (PLS), it was found that the models built using spectral data of canopy based on ASD non-imaging hyperspectrometer and imaging spectrometer both had low precision, which was possibly caused by background such as soil; while the model established from single wheat plant leaves based on the imaging spectrometer had a better effect. At last, the PLS model was established for chlorophyll SPAD value of wheat seedling leaves based on imaging spectrometry and its correlation coefficient R reached 0.8836, and the correlation coefficient R of the relevant model for nitrogen content was 0.8520, suggesting that the superiority of location monitoring of growth at seedling stage of wheat based on hyperspectral imaging is significant.

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