A rapid and nondestructive tea classification method is of great significance in today’s research. This study uses fluorescence hyperspectral technology and machine learning to distinguish Oolong tea by analyzing the spectral features of tea in the wavelength ranging from 475 to 1100 nm. The spectral data are preprocessed by multivariate scattering correction (MSC) and standard normal variable (SNV), which can effectively reduce the impact of baseline drift and tilt. Then principal component analysis (PCA) and t-distribution random neighborhood embedding (t-SNE) are adopted for feature dimensionality reduction and visual display. Random Forest-Recursive Feature Elimination (RF-RFE) is used for feature selection. Decision Tree (DT), Random Forest Classification (RFC), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are used to establish the classification model. The results show that MSC-RF-RFE-SVM is the best model for the classification of Oolong tea in which the accuracy of the training set and test set is 100% and 98.73%, respectively. It can be concluded that fluorescence hyperspectral technology and machine learning are feasible to classify Oolong tea.

Nondestructive detection of tea’s internal quality is of great significance for the processing and storage of tea. In this study, hyperspectral imaging technology is adopted to quantitatively detect the content of tea polyphenols in Tibetan teas by analyzing the features of the tea spectrum in the wavelength ranging from 420 to 1010 nm. The samples are divided with joint x-y distances (SPXY) and Kennard-Stone (KS) algorithms, while six algorithms are used to preprocess the spectral data. Six other algorithms, Random Forest (RF), Gradient Boosting (GB), Adaptive boost (AdaBoost), Categorical Boosting (CatBoost), LightGBM, and XGBoost, are used to carry out feature extractions. Then based on a stacking combination strategy, a new two-layer combination prediction model is constructed, which is used to compare with the four individual regressor prediction models: RF Regressor (RFR), CatBoost Regressor (CatBoostR), LightGBM Regressor (LightGBMR) and XGBoost Regressor (XGBoostR). The experimental results show that the newly-built Stacking model predicts more accurately than the individual regressor prediction models. The coefficients of determination $R_c^2$ and$R_p^2$ for the prediction of Tibetan tea polyphenols are 0.9709 and 0.9625, and the root mean square error RMSEC and RMSEP are 0.2766 and 0.3852 for the new model, respectively, which shows that the content of Tibetan tea polyphenols can be determined with precision.

Sunflower is an important crop, and the vitality and moisture content of sunflower seeds have an important influence on the sunflower’s planting and yield. By employing hyperspectral technology, the spectral characteristics of sunflower seeds within the wavelength range of 384–1034 nm were carefully analyzed with the aim of achieving effective prediction of seed vitality and moisture content. Firstly, the original hyperspectral data were subjected to preprocessing techniques such as Savitzky–Golay smoothing, standard normal variable correction (SNV), and multiplicative scatter correction (MSC) to effectively reduce noise interference, ensuring the accuracy and reliability of the data. Subsequently, principal component analysis (PCA), extreme gradient boosting (XGBoost), and stacked autoencoders (SAE) were utilized to extract key feature bands, enhancing the interpretability and predictive performance of the data. During the modeling phase, random forests (RFs) and LightGBM algorithms were separately employed to construct classification models for seed vitality and prediction models for moisture content. The experimental results demonstrated that the SG-SAE-LightGBM model exhibited outstanding performance in the classification task of sunflower seed vitality, achieving an accuracy rate of 98.65%. Meanwhile, the SNV-XGBoost-LightGBM model showed remarkable achievement in moisture content prediction, with a coefficient of determination ($R^2$) of 0.9715 and root mean square error ($RMSE$) of 0.8349. In conclusion, this study confirms that the fusion of hyperspectral technology and multivariate data analysis algorithms enables the accurate and rapid assessment of sunflower seed vitality and moisture content, providing robust tools and theoretical support for seed quality evaluation and agricultural production practices. Furthermore, this research not only expands the application of hyperspectral technology in unraveling the intrinsic vitality characteristics of sunflower seeds but also possesses significant theoretical and practical value.

The accurate classification of Amanita is helpful to its research on biological control and medical value, and it can also prevent mushroom poisoning incidents. In this paper, we constructed the Bilinear convolutional neural networks (B-CNN) with attention mechanism model based on transfer learning to realize the classification of Amanita. When the model is trained, the weight on ImageNet is used for pre-training, and the Adam optimizer is used to update network parameters. In the test process, images of Amanita at different growth stages were used to further test the generalization ability of the model. After comparing our model with other models, the results show that our model greatly reduces the number of parameters while achieving high accuracy (95.2%) and has good generalization ability. It is an efficient classification model, which provides a new option for mushroom classification in areas with limited computing resources.

The cherry tomato has an important economic value and an increasingly broad market, and the chlorophyll content of cherry tomato leaves can directly reflect the plant’s photosynthetic ability, thus indirectly reflecting its growth status. Therefore, this paper proposes a regression detection method for chlorophyll in cherry tomato leaves by combining machine learning and hyperspectral technology to realize non-destructive, fast, and more accurate detection. Firstly, Moving-Average (MA) preprocessing was chosen as the pretreatment method for this paper, and three regression models of principal component regression (PCR), random forest (RF), and partial least squares regression (PLSR) were established with leaf chlorophyll under different nitrogen concentrations. The CARS_PLSR algorithm has the highest prediction accuracy with accuracy, precision, RMSEC, and RMSEP of 0.8790, 0.9187, 2.9581, and 2.5578, respectively. The study examined the impact of various nitrogen concentrations on the chlorophyll content of cherry tomato leaves, and it was found that the high concentration of nitrogen inhibited the SPAD value of cherry tomato leaves more than that of the low concentration, and the optimal concentration of nitrogen fertilization for tomatoes was 300 mg·L⁻¹. Finally, a regression model was established by using CARS-PLSR combined with the pseudo-color map technology, and a distribution map of chlorophyll content in different SPAD value gradients of cherry tomato leaves was obtained, which could visualize the distribution of chlorophyll and its distribution sites in the leaves and understand the growth status of cherry tomatoes. The distribution of chlorophyll content in different SPAD values of cherry tomato leaves was obtained by using the CARS-PLSR regression model combined with pseudo-color map technology, which can visualize the distribution of chlorophyll in the leaves and the parts of distribution and understand the growth condition of cherry tomatoes. Finally, the optimal model is applied in conjunction with a sprayer to automate fertilizer application.