This study, leveraging near-infrared spectroscopy technology and integrating vegetation index analysis, aims to develop a hyperspectral imaging-based non-destructive inspection technique for swift monitoring of crop chlorophyll content by rapidly predicting leaf SPAD. To this end, a high-precision spectral prediction model was first established under laboratory conditions using ex situ lyophilized Leaf samples. This model provides a core algorithmic foundation for future non-destructive field applications. A systematic study was conducted to develop prediction models for leaf SPAD values of Korla fragrant pear at different growth stages (fruit-setting period, fruit swelling period and Maturity period). This involved comparing various spectral preprocessing algorithms (AirPLS, Savitzky–Golay, Multiplicative Scatter Correction, FD, etc.) and CARS Feature Selection methods for the screening of optimal spectral feature band. Subsequently, models were constructed using BP Neural Network and Support Vector Regression algorithms. The results showed that leaf samples at different growth stages exhibited significant differences in their spectral features within the 5000–7000 cm⁻¹ (effective features for predicting chlorophyll (SPAD)) and 7000–8000 cm⁻¹ (moisture absorption valley) bands. The Savitzky–Golay+FD (Savitzky–Golay smoothing combined with first-order derivative (FD)) preprocessing algorithm performed optimally in feature extraction. Growth period specificity models significantly outperformed whole growth period models, with the optimal models for the fruit-setting period and fruit swelling period being FD-CARS-BP (Coefficient of determination (R²) > 0.86), and the optimal model for the Maturity period being Savitzky–Golay-FD+Savitzky–Golay-CARS-BP (Coefficient_of_determination (R²) = 0.862). Furthermore, joint modeling of characteristic spectra and vegetation indices further improved prediction performance (Coefficient of determination (R²) > 0.85, Root Mean Square Error (RMSE) 2.5). This study presents a reliable method for non-destructive monitoring of chlorophyll content in Korla fragrant pears, offering significant value for nutrient management and stress early warning in precision agriculture.

Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) and phosphorus (P). Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” We systematically collected a total of 9000 leaf samples from Korla fragrant pear orchards and acquired spectral data using a benchtop near-infrared spectrometer. After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. Multiplicative scatter correction (MSC) and first derivative (FD) are synergistically employed for preprocessing to eliminate scattering interference and enhance the resolution of characteristic peaks. Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. Moreover, it elucidates the physiological mechanism by which potassium influences spectral response through the regulation of water metabolism.

The efficient and non-destructive inspection of the chlorophyll content of grey jujube leaf is of great significance for its growth surveillance and nutritional diagnosis. Near-infrared spectroscopy combined with chemometric methods provides an effective approach to achieve this goal. This study took grey jujube leaves as the research object, systematically collected near-infrared spectral data in the range of 4000–10,000 cm⁻¹, and simultaneously measured their soil and plant analyzer development (SPAD) value as a reference index for chlorophyll content. Through various pretreatment and their combination methods on the original spectrum—smooth, standard normal variable transformation (SNV), first derivative (FD), second derivative (SD), smooth + first derivative (Smooth + FD), smooth + second derivative (Smooth + SD), standard normal variable transformation + first derivative (SNV + FD), standard normal variable transformation + second derivative (SNV + SD)—the effects of different methods on the quality of the spectrum and its correlation with SPAD value were compared. The competitive adaptive reweighted sampling algorithm (CARS) was adopted to extract the characteristic wavelength, aiming to reduce data dimensionality and optimize model input. Both BP neural network and RBF neural network prediction models were established, and the model performance under different training functions was compared. The results indicate that after Smooth + FD pretreatment, followed by CARS screening of the characteristic wavelength, the BP neural network model trained using the LBFGS algorithm demonstrated the best performance, with its coefficient of determination (R²) of 0.87 (training set) and 0.85 (validation set), root mean square error (RMSE) of 1.36 (training set) and 1.35 (validation set), and residual prediction deviation (RPD) of 2.81 (training set) and 2.56 (validation set) showing good prediction accuracy and robustness. Research indicates that by combining near-infrared spectroscopy with feature extraction and machine learning methods, the rapid and non-destructive inspection of the grey jujube leaf SPAD value can be achieved, providing reliable technical support for the real-time monitoring of the nutritional status of jujube trees.

In modern agricultural production, accurately estimating the leaf water content (LWC) of Korla fragrant pear is crucial for achieving scientific irrigation and ensuring fruit quality. However, constructing accurate and effective LWC prediction models remains challenging due to limitations in sample selection, spectral feature analysis, and model applicability. To address these issues, this study was conducted to systematically optimize the process. During sample collection, a random split method was employed to divide the dataset into modeling and testing sets at a ratio of 75%:25%. This approach ensures computational efficiency, avoids data leakage, and balances training and evaluation needs, particularly for small- to medium-sized datasets. Specifically, in stage S1, 352 samples were allocated to the modeling set and 108 to the testing set, while in stage S2, 137 and 58 samples were assigned, respectively. The analysis revealed slight differences in LWC distribution and standard deviation between the modeling and testing sets, validating the scientific rigor of dataset division. For instance, the LWC distribution in the S1 modeling set ranged from 4.88% to 83.45%, with a standard deviation of 11.33%. The spectral acquisition process within the range of 4000 cm⁻¹ to 10,000 cm⁻¹ exhibited complex absorbance variation trends, showing distinct characteristics across different intervals. Preprocessing techniques such as SG convolution smoothing, MSC, and SNV significantly reduced the absorbance variability and enhanced spectral features. Notably, the selection of LWC feature bands differed markedly between stages S1 and S2. For example, in S1, SNV-SPA (successive projections algorithm) feature bands were concentrated around 5000 cm⁻¹, 6000 cm⁻¹, and 7000 cm⁻¹, whereas their positions shifted significantly in S2, reflecting the growth dynamics of the Korla fragrant pear. During the model-building phase, various algorithms, including Random Forest Regression (RFR), Backpropagation Neural Network (BP), and Support Vector Regression (SVR), were compared. Under different feature selections, the RFR model demonstrated strong predictive ability with determination coefficients (R²) exceeding 0.75 and root mean square errors (RMSE) below 0.7%. Specifically, the SNV-CARS-BP model achieved an R² of 0.81594 in S1, while the SNV-SPA-RFR model reached an R² of 0.817756 in S2, with relative deviations between the predicted and actual values of less than 5%. These results provide robust support for the precise LWC monitoring of Korla fragrant pear and offer valuable insights for subsequent research.

This study performed genome-wide identification and functional characterization of BAHD genes in Korla fragrant pear and investigated their roles in the de