Vis/NIR reflectance spectroscopy for hybrid rice variety identification and chlorophyll content evaluation for different nitrogen fertilizer levels.

Hao Zhang,Guangyu Zhao,Shiming Zhu,Ting Peng,Sune Svanberg,Zheng Duan,Yiyun Li,Wei Fu,Quanzhi Zhao,Jiandong Hu

doi:10.1098/rsos.191132

Abstract

Nitrogen is one of the most important nutrient indicators for the growth of crops, and is closely related to the chlorophyll content of leaves and thus influences the photosynthetic ability of the crops. In this study, five hybrid rice varieties were cultivated during one entire growing period in one experimental field supplied with six nitrogen fertilizer levels. Visible and near infrared (vis/NIR) reflectance spectroscopy combined with multivariate analysis was used to identify hybrid rice varieties and nitrogen fertilizer levels, as well as to detect chlorophyll content associated with nitrogen levels. The support vector machine (SVM) algorithm was applied to identify five varieties of hybrid rice and six levels of nitrogen fertilizer. The results demonstrated that different varieties of hybrid rice for each nitrogen level can be well distinguished except for the highest nitrogen level, and no nitrogen level for each rice variety can be completely identified from the other five nitrogen levels. Further, 12 spectral indices combined with partial least square (PLS) analysis were applied for estimating chlorophyll content of rice leaves from plants subjected to different nitrogen levels, and a root mean square error of cross-validation (RMSECV) of 0.506, a coefficient of determination (R2) of 97.8% and a ratio of performance to deviation (RPD) of 4.6 for all rice varieties indicated this as a preferable procedure. This study demonstrates that Vis/NIR spectroscopy can have a great potential for identification of rice varieties and evaluation of nitrogen fertilizer levels.

Highlights

Paddy rice dominates overall crop production on the global scale, and China is the world’s largest producer of rice
Reflectance spectral measurements coupled with multivariate analysis were shown to be quite powerful in identifying the rice varieties and evaluating chlorophyll content in hybrid rice under different nitrogen fertilizer levels
The support vector machine (SVM) algorithm based on the reflectance spectrum ranging from 450–850 nm showed a better discrimination performance for identifying different rice varieties; it was found to be inadequate for different nitrogen fertilizer levels

Summary

Introduction

Paddy rice dominates overall crop production on the global scale, and China is the world’s largest producer of rice. To achieve high-yielding rice and solve the problems associated with food shortage in developing countries, hybrid rice was first developed by L.P. Yuan in China and has been investigated for more than 50 years. Various varieties of super hybrid rice were bred, such as ChaoYou 1000, which in 2018 has set a new yield record of about 1065 kg acre–1 The leaf chlorophyll content (LCC) is usually regarded as one main indicator to estimate the photosynthetic ability, health status and nitrogen nutrition level of crops. The leaf nitrogen content (LNC) plays a key role in indicating the photosynthetic status of crops. Monitoring the chlorophyll content can provide important information on the photosynthesis and nitrogen nutrition status of crops

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