Comparative research on multiple estimation models of magnesium content in honey pummelo leaves based on spectral indices

【Objectives】Guanxi honey pummelo [Citrus grandis (L.) Osbeck] has a large planting area in Pinghe County, Fujian Province, China. For a long time, honey pummelo farmers have often only focused on the application of fertilizers containing large amounts of N, P, and K elements, often neglecting the application of other medium and trace elements. Magnesium (Mg) is an important intermediate mineral element in plants and is crucial for the growth of honey pummelo. Mg deficiency in honey pummelo leaves can lead to yellowing of leaves, resulting in a decrease in yield and quality. Hyperspectral technology can directly and quantitatively analyze weak spectral differences, which would provide a good method for quantitatively analyzing the correlation between plant Mg contents and spectral parameters. By constructing the estimation model of Mg content in honey pummelo leaves based on spectral analysis, it could provide a theoretical basis for monitoring and rapid non-destructive diagnosis of Mg content in pummelo leaves.【Methods】Based on hyperspectral data of honey pummelo leaves and mea-sured data of leaf Mg content, the original spectral and first- order derivative spectral characteristic bands and spectral characteristic indices (difference spectral index (DSI), ratio spectral index (RSI), and normalized difference spectral index (NDSI)) were analyzed and extracted. Single variable estimation models, partial least squares regression model (PLS), backpropagation neural network regression model (BPNN), random forest regression model (RF) and support vector machine regression model (SVM) for honey pummelo leaf Mg content were established, and the best estimation model for honey pummelo leaf Mg content was evaluated and determined.【Results】Through correlation analysis between the Mg content of honey pummelo leaves and their original and first derivative spectra, it was found that the original spectral reflectance of honey pummelo leaves was negatively correlated with the Mg content of the leaves, but the correlation coefficients were small and did not reach a significant level within the wavelength range of 350-1050 nm. The maximum negative correlation coefficient was only -0.23 at the wavelength of 693 nm. It could be seen that the original spectral estimation of Mg content in honey pummelo leaves was not effective, making it difficult to estimate the magnesium content in honey pummelo leaves. However, the first-order derivative spectrum showed significant positive or negative correlation with leaf magnesium content at multiple wavelengths, with 900 nm, 821 nm, and 363 nm being the wavelengths with the highest absolute correlation coefficient. The correlation between Mg content and spectral index in honey pummelo leaves was visualized and analyzed using contour maps. The analysis revealed that the maximum absolute r-values of the spectral index of the original spectrum and the Mg content in honey pummelo leaves were 0.44 (DSI1010, 1020), -0.44 (RSI550, 700) and 0.44 (NDSI560, 700), respectively. The spectral index of the first- order derivative spectrum and the maximum r- value of Mg content in honey pummelo leaves were 0.62 (NDSI′720, 900), 0.59 (DSI′360, 900) and -0.66 (RSI′880, 900), respectively. The polynomial estimation model with a larger coefficient of determination R² (R²≥0.37) was constructed using RSI′880, 900, NDSI′720, 900, NDSI′730, 900, NDSI′850, 900, RSI′720, 900, DSI′360, 900, RSI′730, 900 as independent variables, the determination coefficients R² were of 0.44, 0.42, 0.42, 0.39, 0.38, 0.38, and 0.37, respectively. The performance of using spectral index to predict Mg content in honey pummelo leaves was poor. We selected variables with relatively good correlation among the above spectral indices and constructed multiple hyperspectral estimation models for Mg content in honey pummelo leaves using PLS, BPNN, RF, and SVM methods, and conducted comparative verification. The determination coefficients (R2 ) of the established PLS, BPNN, RF, and SVM models for estimating Mg content in pummelo leaves were 0.64, 0.65, 0.85 and 0.72, respectively, and the root mean square errors (RMSE) were 0.44, 0.41, 0.37 and 0.39, respectively, and the RE (relative error) values were 17.23%, 15.66%, 12.18% and 12.55%, respectively. The R2 values of the validation models were 0.61, 0.66, 0.89 and 0.70, respectively. The RMSE values of the validation models were 0.48, 0.46, 0.34 and 0.43, respectively. The RE values of the validation models were 17.64%, 15.95%, 11.93% and 11.59%, respectively. Compared with others, the RF validation model had a higher R2 , lower RMSE, and lower RE, indicating that the RF method could better estimate the Mg content in honey pummelo leaves. The order of estimating and validating model accuracy was RF＞SVM＞BPNN＞PLS.【Conclusions】Four hyperspectral models for Mg content in honey pummelo leaves were compared. After comprehensive estimation and validation of model accuracy, the RF estimation model showed slightly higher accuracy than PLS, BPNN, and SVM estimation models. The results would provide a technical basis for rapid and non-destructive estimation of Mg content in honey pummelo leaves using spectroscopy. Subsequent research needs to elevate its study to the canopy level through ground or aerial hyperspectral remote sensing, and repeatedly verify it under different geographical spaces, different periods, and different sample sizes. At the same time, it isnecessary to conduct in-depth research on the mechanism of spectral influence on leaf magnesium content. These would help provide technical references for precise nutrient fertilization of honey pummelo.