Abstract
Nitrogen (N) is important for the growth of crops. Leaf nitrogen content (LNC) serves as a crucial indicator of the growth status of crops and can help determine the dose of N fertilizer. Laser-induced fluorescence (LIF) technology and the reflectance spectra of crops are widely used to detect the biochemical content of leaves. Many vegetation indices (VIs) and fluorescence parameters have been developed to estimate LNC. However, the comparison among VIs and between fluorescence parameters and VIs has been rarely studied in the estimation of LNC. In this study, the performances of several published empirical VIs and fluorescence parameters for the estimation of paddy rice LNC were analyzed using the support vector machine (SVM) algorithm. Then, the optimal VIs (TVI, MTVI1, MTVI2, and MSAVI) and fluorescence parameters (F735/F460 and F685/F460), which were suitable for LNC monitoring in this study, were chosen. In addition, the combination of the VIs and fluorescence parameters was proposed as the input variables in the SVM model and used to estimate the LNC. Experimental results exhibited the promising potential of the LIF technology combined with reflectance for the accurate estimation of LNC, which provided guidance for monitoring the LNC.
Highlights
Paddy rice is an important crop and a daily necessity to one-third of the world population
The experimental results demonstrated that TVI, MTVI1, MTVI2, and MSAVI displayed higher correlations (R2 values were 0.79, 0.77, 0.78, and 0.70 for 2014 and 0.72, 0.73, 0.65, and 0.66 for 2015) than the other vegetation indices (VIs)
The results demonstrated that the F735/F460 and F685/ F460 were superior to the other fluorescence parameters in estimating leaf nitrogen content (LNC)
Summary
Paddy rice is an important crop and a daily necessity to one-third of the world population. In China, approximately 30 million hectares of farming land are utilized each year to cultivate paddy rice. China is the leading producer of paddy rice in the world [1, 2]. Numerous studies have indicated that nitrogen (N) is a major nutrient element in crops and closely related to cereal crop yield [3,4,5]. Related studies have demonstrated that leaf nitrogen content (LNC) is a crucial indicator for estimating the dose of N level in crops. Numerous passive and active remote sensing technologies have been utilized to monitor LNC in cereal crops [6,7,8,9]
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