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The complex backgrounds of crop disease images and the small contrast between the disease area and the background can easily cause confusion, which seriously affects the robustness and accuracy of apple disease- identification models. To solve the above problems, this paper proposes a Vision Transformer-based lightweight apple leaf disease- identification model, ConvViT, to extract effective features of crop disease spots to identify crop diseases. Our ConvViT includes convolutional structures and Transformer structures; the convolutional structure is used to extract the global features of the image, and the Transformer structure is used to obtain the local features of the disease region to help the CNN see better. The patch embedding method is improved to retain more edge information of the image and promote the information exchange between patches in the Transformer. The parameters and FLOPs (Floating Point Operations) of the model are significantly reduced by using depthwise separable convolution and linear-complexity multi-head attention operations. Experimental results on a complex background of a self-built apple leaf disease dataset show that ConvViT achieves comparable identification results (96.85%) with the current performance of the state-of-the-art Swin-Tiny. The parameters and FLOPs are only 32.7% and 21.7% of Swin-Tiny, and significantly ahead of MobilenetV3, Efficientnet-b0, and other models, which indicates that the proposed model is indeed an effective disease-identification model with practical application value.

Anthracnose, brown spot, mites, black rot, downy mildew, and leaf blight are six common grape leaf pests and diseases, which cause severe economic losses to the grape industry. Timely diagnosis and accurate identification of grape leaf diseases are decisive for controlling the spread of disease and ensuring the healthy development of the grape industry. This paper proposes a novel recognition approach that is based on improved convolutional neural networks for the diagnoses of grape leaf diseases. First, based on 4,023 images collected in the field and 3,646 images collected from public data sets, a data set of 107,366 grape leaf images is generated via image enhancement techniques. Afterward, Inception structure is applied for strengthening the performance of multi-dimensional feature extraction. In addition, a dense connectivity strategy is introduced to encourage feature reuse and strengthen feature propagation. Ultimately, a novel CNN-based model, namely, DICNN, is built and trained from scratch. It realizes an overall accuracy of 97.22% under the hold-out test set. Compared to GoogLeNet and ResNet-34, the recognition accuracy increases by 2.97% and 2.55%, respectively. The experimental results demonstrate that the proposed model can efficiently recognize grape leaf diseases. Meanwhile, this study explores a new approach for the rapid and accurate diagnosis of plant diseases that establishes a theoretical foundation for the application of deep learning in the field of agricultural information.

Black rot, Black measles, Leaf blight and Mites of grape are four common grape leaf diseases that seriously affect grape yield. However, the existing research lacks a real-time detecting method for grape leaf diseases, which cannot guarantee the healthy growth of grape plants. In this article, a real-time detector for grape leaf diseases based on improved deep convolutional neural networks is proposed. This article first expands the grape leaf disease images through digital image processing technology, constructing the grape leaf disease dataset (GLDD). Based on GLDD and the Faster R-CNN detection algorithm, a deep-learning-based Faster DR-IACNN model with higher feature extraction capability is presented for detecting grape leaf diseases by introducing the Inception-v1 module, Inception-ResNet-v2 module and SE-blocks. The experimental results show that the detection model Faster DR-IACNN achieves a precision of 81.1% mAP on GLDD, and the detection speed reaches 15.01 FPS. This research indicates that the real-time detector Faster DR-IACNN based on deep learning provides a feasible solution for the diagnosis of grape leaf diseases and provides guidance for the detection of other plant diseases.

(1) Background: Dyes play an important role in food, medicine, textile, and other industries, which make human life more colorful. With the increasing demand for food safety, the development of natural dyes becomes more and more attractive. (2) Methods: The literature was searched using the electronic databases PubMed, Web of Science, and SciFinder and this scoping review was carried out following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). (3) Results: 248 articles were included in this review. This review summarizes the research progress on natural dyes in the last ten years. According to structural features, natural dyes mainly include carotenoids, polyphenols, porphyrins, and alkaloids, and some of the newest dyes are summarized. Some pharmacological activities of carotenoids, anthocyanin, curcumin, and betalains in the last 10 years are summarized, and the biological effects of dyes regarding illumination conditions. The disadvantages of natural dyes, including sources, cost, stability, and poor bioavailability, limit their application. Here, some feasible strategies (potential resources, biotechnology, new extraction and separation strategies, strategies for improving stability) are described, which will contribute to the development and utilization of natural dyes. (4) Conclusion: Natural dyes show health benefits and potential in food additives. However, it is necessary for natural dyes to pass toxicity tests and quality tests and receive many regulatory approvals before their final entry into the market as food colorants or as drugs.

This study investigates the synergistic effect and mechanism of gelling materials with blast furnace slag (BFS), steel slag (SS) and desulphurization gypsum (DG) as the main components on the hardening of heavy metal ions by lead and zinc tailings. It is found that lead and zinc tailing (LZT) is mainly composed of dolomite and quartz and contain small amounts of calcium, aluminum, iron, magnesium and other elements as well as heavy metals such as lead and zinc. By the mechanical activation method, it is found that the lead and zinc tailings powder has the largest specific surface area and the highest activity index when the ball milling time is 2 h. At a hardening timepoint of 28 d, the calcite crystals in the samples are intertwined with the amorphous C-S-H gel (C-S-H gels are mainly composed of 3CaO∙SiO2 and 2CaO∙SiO2), which enhances the structural strength of the samples. The chemical reaction analysis confirmed that the formation of calcite is a major driver for the hydration reaction of the steel slag–desulphurization gypsum (SSSDG) system. Overall, the slag, steel slag and desulphurization gypsum solid waste-based gelling materials have synergistic effects in hardening heavy metals by limiting the leaching of metal ions, adsorbing metal ions and hardening heavy metals, and facilitating the hydration process through the formation of compound salt precipitates.

With the accelerated urbanization process in the Yangtze River Delta region, shallow groundwater has received increasing attention. In this work, the exceedances of the ammonium nitrogen (NH 3 -N) and chemical oxygen demand (COD Mn ) in shallow groundwater in the central typical area of the Yangtze River Delta were investigated. With the utilization of the national monitoring well (QY10A) as a focal point, a combination of methods, including onsite sampling, hydrogeological surveys, leaching tests, water quality analysis, and isotope tracing, was employed to comprehensively examine groundwater pollution. The study addressed the history of groundwater exploitation, changes in surface water quality, and the influence of stratigraphic structure on groundwater contamination. It has been observed that the NH 3 -N levels in the silty chalky clay layer and the lower grayish black chalky clay layer in the study area are notably elevated, with concentrations reaching up to 87.5 mg/kg and 97.4 mg/kg in some boreholes. The NH 3 -N concentration in the silty clay with silty sand can reach as high as 87.2 mg/kg, whereas the concentration is lower in the underlying layers. In the other strata, NH 3 -N values remain low. The results indicated that the NH 3 -N and COD Mn in the QY10A monitoring well resulted primarily from the inherently high organic nitrogen content in the local geological environment rather than from anthropogenic sources such as industrial parks, domestic sewage, or agricultural activities. This finding highlights the critical role of geological conditions in influencing groundwater quality, emphasizing the necessity of considering these natural factors in pollution prevention and management strategies. Our research provides valuable insights for environmental management in similar geological settings and demonstrates the importance of scientifically rigorous methods for advancing environmental research and policy-making.

Remarkable inter-class similarity and intra-class variability of tomato leaf diseases seriously affect the accuracy of identification models. A novel tomato leaf disease identification model, DWTFormer, based on frequency-spatial feature fusion, was proposed to address this issue. Firstly, a Bneck-DSM module was designed to extract shallow features, laying the groundwork for deep feature extraction. Then, a dual-branch feature mapping network (DFMM) was proposed to extract multi-scale disease features from frequency and spatial domain information. In the frequency branch, a 2D discrete wavelet transform feature decomposition module effectively captured the rich frequency information in the disease image, compensating for spatial domain information. In the spatial branch, a multi-scale convolution and PVT (Pyramid Vision Transformer)-based module was developed to extract the global and local spatial features, enabling comprehensive spatial representation. Finally, a dual-domain features fusion model based on dynamic cross-attention was proposed to fuse the frequency-spatial features. Experimental results on the tomato leaf disease dataset demonstrated that DWTFormer achieved 99.28% identification accuracy, outperforming most existing mainstream models. Furthermore, 96.18% and 99.89% identification accuracies have been obtained on the AI Challenger 2018 and PlantVillage datasets. In-field experiments demonstrated that DWTFormer achieved an identification accuracy of 97.22% and an average inference time of 0.028 seconds in real plant environments. This work has effectively reduced the impact of inter-class similarity and intra-class variability on tomato leaf disease identification. It provides a scalable model reference for fast and accurate disease identification.

The body dimension measurement of large animals plays a significant role in quality improvement and genetic breeding, and the non-contact measurements by computer vision-based remote sensing could represent great progress in the case of dangerous stress responses and time-costing manual measurements. This paper presents a novel approach for three-dimensional digital modeling of live adult Qinchuan cattle for body size measurement. On the basis of capturing the original point data series of live cattle by a Light Detection and Ranging (LiDAR) sensor, the conditional, statistical outliers and voxel grid filtering methods are fused to cancel the background and outliers. After the segmentation of K-means clustering extraction and the RANdom SAmple Consensus (RANSAC) algorithm, the Fast Point Feature Histogram (FPFH) is put forward to get the cattle data automatically. The cattle surface is reconstructed to get the 3D cattle model using fast Iterative Closest Point (ICP) matching with Bi-directional Random K-D Trees and a Greedy Projection Triangulation (GPT) reconstruction method by which the feature points of cattle silhouettes could be clicked and calculated. Finally, the five body parameters (withers height, chest depth, back height, body length, and waist height) are measured in the field and verified within an accuracy of 2 mm and an error close to 2%. The experimental results show that this approach could be considered as a new feasible method towards the non-contact body measurement for large physique livestock.

Behavior is one of the important factors reflecting the health status of dairy cows, and when dairy cows encounter health problems, they exhibit different behavioral characteristics. Therefore, identifying dairy cow behavior not only helps in assessing their physiological health and disease treatment but also improves cow welfare, which is very important for the development of animal husbandry. The method of relying on human eyes to observe the behavior of dairy cows has problems such as high labor costs, high labor intensity, and high fatigue rates. Therefore, it is necessary to explore more effective technical means to identify cow behaviors more quickly and accurately and improve the intelligence level of dairy cow farming. Automatic recognition of dairy cow behavior has become a key technology for diagnosing dairy cow diseases, improving farm economic benefits and reducing animal elimination rates. Recently, deep learning for automated dairy cow behavior identification has become a research focus. However, in complex farming environments, dairy cow behaviors are characterized by multiscale features due to large scenes and long data collection distances. Traditional behavior recognition models cannot accurately recognize similar behavior features of dairy cows, such as those with similar visual characteristics, i.e., standing and walking. The behavior recognition method based on 3D convolution solves the problem of small visual feature differences in behavior recognition. However, due to the large number of model parameters, long inference time, and simple data background, it cannot meet the demand for real-time recognition of dairy cow behaviors in complex breeding environments. To address this, we developed an effective yet lightweight model for fast and accurate dairy cow behavior feature learning from video data. We focused on four common behaviors: standing, walking, lying, and mounting. We recorded videos of dairy cow behaviors at a dairy farm containing over one hundred cows using surveillance cameras. A robust model was built using a complex background dataset. We proposed a two-pathway X3DFast model based on spatiotemporal behavior features. The X3D and fast pathways were laterally connected to integrate spatial and temporal features. The X3D pathway extracted spatial features. The fast pathway with R(2 + 1)D convolution decomposed spatiotemporal features and transferred effective spatial features to the X3D pathway. An action model further enhanced X3D spatial modeling. Experiments showed that X3DFast achieved 98.49% top-1 accuracy, outperforming similar methods in identifying the four behaviors. The method we proposed can effectively identify similar dairy cow behaviors while improving inference speed, providing technical support for subsequent dairy cow behavior recognition and daily behavior statistics.

Garlic (Allium sativa L.) is a traditional plant with antimicrobial activity. This study aimed to discover new antifungal peptides from garlic, identify their structure, and explore the antimicrobial mechanism. Peptides were separated by chromatography and identified by MALDI-TOF analysis. Structure and conformation were characterized by CD spectrum and NMR analysis. Mechanism studies were conducted by SEM, membrane depolarization, and transcriptomic analysis. The cytotoxicity to mammalian cells as well as drug resistance development ability were also evaluated. A novel antifungal peptide named NpRS with nine amino acids (RSLNLLMFR) was obtained. It was a kind of cationic peptide with a α-helix as the dominant conformation. NOESY correlation revealed a cyclization in the molecule. The peptide significantly inhibited the growth of Candida albicans. The mechanism study indicated that membrane destruction and the interference of ribosome-related pathways might be the main mechanisms of antifungal effects. In addition, the resistance gene CDR1 for azole was down-regulated and the drug resistance was hardly developed in 21 days by the serial passage study. The present study identified a novel antifungal garlic peptide with low toxicity and provided new mechanism information for the peptide at the gene expression level to counter drug resistance.