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This study aimed to develop a method based on hyperspectral imaging (400–1 000 nm) combined with chemometrics to identify the diverse geographical origins of Lycium barbarum fruit. Hyperspectral images of L. barbarum fruit from Ningxia, Gansu, Inner Mongolia, Qinghai and Xinjiang were acquired, and spectral data was extracted from the region of interest (ROI) by threshold segmentation method. Multiple preprocessing methods were employed to eliminate the interference information from the spectra, and the results showed that the discriminant model based on normalized reflectance spectrum (NR) exhibited better performance. Furthermore, the successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), particle swarm optimization (PSO), iteratively retaining informative variables (IRIV), and CARS + IRIV were used to select characteristic wavelengths. The results showed that the simplified model based on the wavelengths selected by CARS + IRIV had the best performance. In the models ranging from binary to quintuple classifications, the selected characteristic wavelengths accounted for only 15.6% to 27.7% of the full spectra. The prediction accuracy was 97.7%, 90.9%, 89.2%, and 87.1%, respectively. In addition, a confusion matrix was employed to visualize the optimal simplified classification model in order to intuitively distinguish the classification categories. Satisfactory sensitivity, specificity and Kappa coefficients were obtained in classifying L. barbarum. The results illustrated that hyperspectral imaging technology combined with chemometric methods could effectively identify the geographical origin of L. barbarum and provide crucial technical support for the development of the L. barbarum industry.

This study explored the feasibility of applying visible and near-infrared (400?1 000 nm), and mid- and short-wave near infrared (900?1 700 nm) spectroscopy for detecting Camellia oleifera fruit with different degrees of natural mildew. The near infrared spectra of the equatorial shady and sunny sides, and the junction surfaces of samples with different degrees of mildew were collected in two wavelength bands. The average spectra were analyzed by principal component analysis (PCA), revealing that samples with different mildew degrees could be clustered into different groups, and the first and second principal components (PC1 and PC2) were effective in distinguishing the samples in each category. The full-spectrum partial least squares-discriminant analysis (PLS-DA) model based on the original spectra performed better than of its counterpart based on the preprocessed spectra. In the selection of characteristic wavelengths, successive projections algorithm (SPA) was found to be superior to PC loadings in establishing the simplified models for both spectral ranges. The correct classification rate and kappa coefficient were 84.4% and 0.766 7 for the prediction set, respectively. Based on the observation of confusion matrices for the prediction set, the specificities of the two optimal simplified models for the prediction of each degree of mildew were equivalent to each other and above 0.84. However, mid- and short-wave near infrared spectra in the wavelength range of 900–1 700 nm provided slightly higher sensitivity (0.72) in classifying samples with moderate degree of mildew. Our study showed that near-infrared spectroscopy could be used to detect the degree of natural mildew of C. oleifera fruit, and visible and near-infrared spectroscopy showed comparable results to mid- and short-wave near infrared spectroscopy. Considering its lower cost, visible and near-infrared spectroscopy has a better application prospect for real-time detection.

Pharmaceutical contaminants are causing great attention for the public people, urgently requiring to be eliminated from the environment. Herein, commercial pharmaceutical carbamazepine was chosen as a model to be degraded by a heterogeneous Fenton system using natural pyrite as catalyst. The catalyst was characterized by X‐ray diffraction apparatus, high‐resolution transmission electron microscopy, and X‐ray photoelectron spectroscopy. The elements composition and chemical states of pyrite changed significantly after the Fenton reaction, which promoted the production of active species in the electron transfer process. The degradation efficiency of carbamazepine (2.5 mg/L) can reach 99.71% after 30 min with 0.3 g/L of pyrite and 5 mM of H2O2, respectively. The reaction system was suitable for a wide range of pH (3–9). The environmental adaptability examination of pyrite showed that humic acid (HA) had inhibitory effects on the degradation of carbamazepine. While the changes of degradation efficiency of carbamazepine were inconspicuous in the presence of NO3− and SO42−, indicating strong anti‐interference to environment common anions. Free‐radical capturers suggested that hydroxyl radical (∙OH), superoxide radical (∙O2−), and singlet oxygen (1O2) participated in the degradation of carbamazepine. This research has offered a new strategy of "treating wastes with wastes" to use pyrite to remove emerging pollutants. [ABSTRACT FROM AUTHOR]

Tea cultivation holds significant economic value, yet the leaves of tea plants are frequently susceptible to various pest and disease infestations. Consequently, there is a critical need for research focused on precisely and efficiently detecting these threats to tea crops. The investigation of a model capable of effectively identifying pests and diseases in tea plants is often hindered by challenges, such as limited datasets of pest and disease samples and the small size of detection targets. To address these issues, this study has chosen TLB, a common pest and disease in tea plants, as the primary research subject. The approach involves the application of transfer learning in conjunction with data augmentation as a fundamental methodology. This technique entails transferring knowledge acquired from a comprehensive source data domain to the model, aiming to mitigate the constraints of limited sample sizes. Additionally, to tackle the challenge of detecting small targets, this study incorporates the decoupling detection head TSCODE and integrates the Triplet Attention mechanism into the E-ELAN structure within the backbone to enhance the model's focus on the TLB's small targets and optimize detection accuracy. Furthermore, the model's loss function is optimized based on the Wasserstein distance measure to mitigate issues related to sensitivity in localizing small targets. Experimental results demonstrate that, in comparison to the conventional YOLOv7 tiny model, the proposed model exhibits superior performance on the TLB small sample dataset, with precision increasing by 6.5% to 92.2%, recall by 4.5% to 86.6%, and average precision by 5.8% to 91.5%. This research offers an effective solution for identifying tea pests and diseases, presenting a novel approach to developing a model for detecting such threats in tea cultivation. [ABSTRACT FROM AUTHOR]

The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model's learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model's ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities. [ABSTRACT FROM AUTHOR]

An accurate simulation model is crucial for the analysis of the correct modal information of the ginkgo seed–stem system (ginkgo subsystem). This underpins the provision of technical rationale for efficient and low-damage precision vibrational harvesting operations in ginkgo cultivation. In this study, based on the modal parameters of the ginkgo subsystem, a finite element model updating method is proposed to correct the elastic modulus of the stem with the natural frequency of the first bending mode. The large difference in the modal results calculated before and after model updating reveals that model updating is a critical step in the finite element analysis of crop subsystems. Then, an uncertainty parameter modeling method is proposed to investigate the modal variability of the ginkgo subsystem by finite element analysis. The results show that the stem length is a key parameter affecting the variability of natural frequencies, and the seed weight is a minor parameter. The variability of the ginkgo seed's gravity center offset has a negligible effect on the natural frequencies of the system. The first natural frequency of the ginkgo subsystem can be utilized for vibrational harvesting. In addition, since the difference between the upper and lower limits of the first natural frequency of the ginkgo subsystem does not exceed 1 Hz, a specific excitation frequency can cause most ginkgo subsystems to resonate. This result facilitates the determination of precise excitation frequencies for efficient and low-damage ginkgo vibrational harvesting, ensuring both economic and ecological benefits in the management of ginkgo plantations. [ABSTRACT FROM AUTHOR]

We have investigated the scattering properties of an individual core-shell nanoparticle using the Mie theory, which can be tuned to support both electric and magnetic modes simultaneously. In general, the suppression of forward scattering can be realized by the second Kerker condition. Here, a novel mechanism has to be adopted to explain zero-forward scattering, which originates from the complex interactions between dipolar and quadrupolar modes. However, for lossy and lossless core-shell spherical nanoparticles, zero-forward scattering can never be achieved because the real parts of Mie expansion coefficients are always positive. By adding proper gain in dielectric shell, zero-forward scattering can be found at certain incident wavelengths, which means that all electric and magnetic responses in Mie scattering can be counteracted totally in the forward direction. In addition, if the absolute values of dipolar and quadrupolar terms are in the same order of magnitude, the local scattering minimum and maximum can be produced away from the forward and backward directions due to the interacting effect between the dipolar and quadrupolar terms. Furthermore, by adding suitable gain in shell, super-forward scattering can also be realized at certain incident wavelengths. We also demonstrated that anomalously weak scattering or superscattering could be obtained for the core-shell nanoparticles with suitable gain in shell. In particular, for such a choice of suitable gain in shell, we can obtain zero-forward scattering and anomalously weak scattering at the same wavelength as well as super-forward scattering at another wavelength. These features may provide new opportunities for cloaking, plasmonic lasers, optical antennas, and so on.

LiDAR technology has been widely used in forest survey and research, but the high-resolution point cloud data generated by LiDAR equipment also pose challenges in storage and computing. To address this problem, we propose a point cloud simplification method for trees, which considers both higher similarity to the original point cloud and the area of the tree point cloud. The method first determines the optimal search neighborhood using the standard deviation of FPFH information entropy. Based on FPFH information entropy and Poisson disc sampling theory, the point cloud is partitioned and sampled. By optimizing the separation thresholds of significant feature points and less significant feature points using a genetic algorithm with the Hausdorff distance and point cloud area as the objective function, the final simplified point cloud is obtained. Validation with two point cloud data sets shows that the proposed method achieves good retention of the area information of the original point cloud while ensuring point cloud quality. The research provides new approaches and techniques for processing large-scale forest LiDAR scan point clouds, reducing storage and computing requirements. This can improve the efficiency of forest surveys and monitoring. [ABSTRACT FROM AUTHOR]