DEVELOPMENT AND TEST OF RICE NITROGEN NUTRITION INDEX ESTIMATION MODEL BASED ON AIRBORNE MULTI-SPECTRUM

文献类型： 外文期刊

第一作者： Song, Lijuan

作者： Song, Lijuan;Ye, Wanjun;Wang, Shu;Liu, Shubing;Wang, Meixuan;Su, Ge;Bi, Hongwen;Song, Lijuan;Wang, Shu

作者机构：

关键词： Cold-terra rice; Airborne multispectrum; Nitrogen nutrition diagnosis

期刊名称：BANGLADESH JOURNAL OF BOTANY （ 影响因子：0.3； 五年影响因子：0.4 ）

ISSN： 0253-5416

年卷期： 2023 年 52 卷 2 期

页码：

收录情况： SCI

摘要： Both excessive and deficient nitrogen (N) concentration in the growing media can affect the growth, development, yield, and quality of rice. Traditional methods of N determination of plant require destructive and rigorous sampling, which is also time-consuming and laborious. However, rapid and non-destructive nitrogen diagnosis has become an important area of research in precision agriculture. Heilongjiang Province, China is a cold climate rice growing area, where the growth and fertilization of rice follows a definite pattern. In the present study, two varieties of rice (Wuyoudao4 and Songjing9) and as location Heilongjiang Province were selected. Nitrogen diagnosis of rice was carried out based on airborne multi-spectrum methodology. Canopy spectral data of rice at key growth periods were obtained by using a UAV equipped with a multi-spectral camera, and agronomic parameters such as leaf N content and dry matter weight were obtained synchronically. An airborne multispectral canopy normalized vegetation index (NDVI) model for N diagnosis based on its critical concentration curve was established as a nondestructive N diagnosis of rice for cold region. Results showed that canopy NDVI can estimate rice nitrogen nutrition index (NNI) properly over the growth period. The coefficient of determination R-2, root mean square error (RMSE) and standard root mean square error (nRMSE) were compared to determine the best effect of the index model. The interphase nitrogen diagnosis model of WYD-4 based on NDVI for cold region was as follows: NNI=0.3916e(1.0809*NDVI) (RMSE=0.12, nRMSE=12.43%), SJ-9: NNI=0.3325e(1.2705*NDVI) (RMSE=0.10, nRMSE =10.36%), indicating that the established model can better estimate the nitrogen status of rice.

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