ZHANG Ya-kun1, 2, 3, LUO Bin2, 3, PAN Da-yu2, 3, SONG Peng2, 3, LU Wen-chao2, 3, WANG Cheng2, 3, ZHAO Chun-jiang1, 2, 3*
1. School of Electrical and Information, Northeast Agricultural University, Harbin 150030, China
2. Beijing Research Center of Intelligent Equipment for Agriculture，Beijing 100097, China
3. National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
Abstract：Nitrogen is one of the most important fertilizers and closely related to the growth, development, yield and quality of crops. Rapid, accurate and non-destructive assessment of nitrogen content in crops is critical for nutrition diagnosis and growth monitoring. Traditional detection methods of nitrogen content are complicated, time-consuming and destructive, which makes the continuous dynamic monitoring of nitrogen content in time and space impossible. It is a hot topic for rapid and non-destructive estimation of crop nitrogen content based on spectral remote sensing technology in recent years. Nevertheless, existing researches about the estimation of nitrogen content were mostly focused on the original or integer differential spectra (first order, second order). Some studies indicated that the original or integer differential spectra might ignore the effective information, which would influence the estimation accuracy of nitrogen content in crops. Fractional order differential algorithm has the advantages in background noise removal and effective information extraction compared with the integer differential methods. Hence, the objective of this study was to explore the feasibility of detecting nitrogen in crops by fractional order differential algorithm. 256 datasets, which were consisted of canopy spectral data and the relevant canopy nitrogen content (CNC) data, were collected during seedling, flowering, pod and drum stages in soybean plants. The plants were treated with different fertilizer components under pot conditions. 0~2 order differentials of spectral data were computed by Grünwald-Letnikov fractional differential equation with differential interval of 0.1. Afterwards, the correlation between the preprocessed spectra and soybean CNC under different fractional order differential were analyzed using the normalized difference spectral index (NDSI) and ratio spectral index (RSI). The maximums of correlation coefficient between soybean CNC and NDSIα (α is the fractional differential order), and between soybean CNC and RSIα were determined under each fractional order differentials. Simultaneously, the corresponding optimal band combinations of NDSIα(opt) and RSIα(opt) were obtained respectively. Eventually, the estimation models of soybean CNC based on NDSIα(opt) and RSIα(opt) under different fractional order differential were established and compared using linear regression method. The estimation models of soybean CNC based on five common vegetation indices including VOG II, MTCI, DCNI, NDRE and TCARI were also established and compared. The results showed that: (1) With the increasing of differential order, the correlation coefficients between soybean CNC and NDSIα(opt), soybean CNC and RSIα(opt) increased firstly and then decreased in the fractional differential range of 0～2. For NDSIα, the maximum correlation coefficient was obtained between soybean CNC and NDSI0.8(R725, R769) under 0.8 order differential, and the relevant value was 0.875 9. For RSIα, the maximum correlation coefficient was obtained between soybean CNC and RSI0.7 (R548, R767) under 0.7 order differential, and the relevant value was 0.865 1; (2) The useful information in spectral data could be extracted and refined using fractional differential algorithm. Therefore, the sensitivity of spectra to soybean CNC could be enhanced. Specifically, the positive correlation between soybean CNC and the band near red edge platform, and the negative correlation between soybean CNC and near the band near green region were enhanced; (3) Compared with the models developed by integer differential and common vegetation indices, the estimation models based on fractional differential were more accurate. For integer differential, the determination coefficients of calibration (R2C) and prediction (R2P) based on RSI0.7(R548, R767) under 0.7 order differential improved 0.061 9 and 0.016 6 compared with the model based on RSI0 (R725, R769) under 0 order differential, respectively. The relevant root mean square errors of the calibration (RMSEC) and prediction (RMSEP) were reduced 0.552 5 and 0.180 9, respectively. The relevant ratio of prediction to deviation (RPD) increased 0.110 4. For common vegetation indices, the R2C and R2P based on RSI0.7(R548, R767) under 0.7 order differential improved 0.086 6 and 0.025 5 compared with the model based on VOG II, respectively. The relevant RMSEC and RMSEP were reduced 0.757 5 and 0.248 3, respectively. The relevant RPD increased 0.146 88; (4) The model based on the ratio spectral index RSI0.7(R548, R767) under 0.7 order differential had the best performance in estimation of soybean CNC with R2C of 0.748 4, R2P of 0.800 3, RMSEC of 4.752 9, RMSEP of 3.511 1 and RPD of 2.253 7 in this study. The results indicated that fractional differential algorithm had the advantages in the quantitative estimation of soybean CNC, which provided a new view for the estimation of crop nitrogen content based on spectral remote sensing technology.
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