Spot Disease Research Articles

Calculation Method Based on Flower Petal Area and Other Plant Leaf Spot Area

In order to calculate the area of irregular shapes such as plant leaves and diseased spots or flower petals, this paper presents a new method to calculate them by using flood fill algorithm, HSV color space, improved k-means algorithm and morphological operation. First, 501 butterfly petal images and pathological leaf images of Bauhinia and phyllotaxus were collected, and then flood was used Fill algorithm selects the disease-free area and records the selected pixel value. HSV color space conversion is applied to the image to facilitate the segmentation of leaves. Then, the improved k-means algorithm is used to extract the binary image of leaves and record the pixel value of the outer contour with morphological closed operation. Finally, the proportion and truth of the disease spots of plant leaves are obtained by calculating the pixel value and the real value of the rectangle in the sampling area Real area. Compared with the results of artificial labeling, the average accuracy of petal area and lesion area of Phalaenopsis was 96.3% and 96.61%, respectively. It can be seen that the program can calculate the area of irregular shape of plant surface accurately. In conclusion, this method can replace the artificial grid method to calculate the information of plant leaf area and disease proportion, and effectively reduce the work intensity of experimental personnel.

Hyperspectral imaging analysis for early detection of tomato bacterial leaf spot disease.

Recent advancements in hyperspectral imaging (HSI) for early disease detection have shown promising results, yet there is a lack of validated high-resolution (spatial and spectral) HSI data representing the responses of plants at different stages of leaf disease progression. To address these gaps, we used bacterial leaf spot (Xanthomonas perforans) of tomato as a model system. Hyperspectral images of tomato leaves, validated against in planta pathogen populations for seven consecutive days, were analyzed to reveal differences between infected and healthy leaves. Machine learning models were trained using leaf-level full spectra data, leaf-level Vegetation index (VI) data, and pixel-level full spectra data at four disease progression stages. The results suggest that HSI can detect disease on tomato leaves at pre-symptomatic stages and differentiate bacterial disease spots from abiotic leaf spots. Using VI data as features for machine learning improved overall classification performance by 26-37% compared to the direct use of raw data. Critical wavelength bands and VIs varied across disease progression stages, suggesting that pre-symptomatic disease detection relied more on changes in leaf water content (1400nm) and plant defense hormone-mediated responses (750nm) rather than changes in leaf pigments or internal structure (800-900nm), which may become more crucial during symptomatic stages. In conclusion, this study provides valuable insights into the dynamics of bacterial spot disease, revealing the potential benefits of leaf structure segmentation and VI group pattern analysis in HSI studies for the early detection of leaf diseases.

Optimal performance design of bat algorithm: An adaptive multi‐stage structure

AbstractThe bat algorithm (BA) is a metaheuristic algorithm for global optimisation that simulates the echolocation behaviour of bats with varying pulse rates of emission and loudness, which can be used to find the globally optimal solutions for various optimisation problems. Knowing the recent criticises of the originality of equations, the principle of BA is concise and easy to implement, and its mathematical structure can be seen as a hybrid particle swarm with simulated annealing. In this research, the authors focus on the performance optimisation of BA as a solver rather than discussing its originality issues. In terms of operation effect, BA has an acceptable convergence speed. However, due to the low proportion of time used to explore the search space, it is easy to converge prematurely and fall into the local optima. The authors propose an adaptive multi‐stage bat algorithm (AMSBA). By tuning the algorithm's focus at three different stages of the search process, AMSBA can achieve a better balance between exploration and exploitation and improve its exploration ability by enhancing its performance in escaping local optima as well as maintaining a certain convergence speed. Therefore, AMSBA can achieve solutions with better quality. A convergence analysis was conducted to demonstrate the global convergence of AMSBA. The authors also perform simulation experiments on 30 benchmark functions from IEEE CEC 2017 as the objective functions and compare AMSBA with some original and improved swarm‐based algorithms. The results verify the effectiveness and superiority of AMSBA. AMSBA is also compared with eight representative optimisation algorithms on 10 benchmark functions derived from IEEE CEC 2020, while this experiment is carried out on five different dimensions of the objective functions respectively. A balance and diversity analysis was performed on AMSBA to demonstrate its improvement over the original BA in terms of balance. AMSBA was also applied to the multi‐threshold image segmentation of Citrus Macular disease, which is a bacterial infection that causes lesions on citrus trees. The segmentation results were analysed by comparing each comparative algorithm's peak signal‐to‐noise ratio, structural similarity index and feature similarity index. The results show that the proposed BA‐based algorithm has apparent advantages, and it can effectively segment the disease spots from citrus leaves when the segmentation threshold is at a low level. Based on a comprehensive study, the authors think the proposed optimiser has mitigated the main drawbacks of the BA, and it can be utilised as an effective optimisation tool.

New Method for Tomato Disease Detection Based on Image Segmentation and Cycle-GAN Enhancement

A major concern in data-driven deep learning (DL) is how to maximize the capability of a model for limited datasets. The lack of high-performance datasets limits intelligent agriculture development. Recent studies have shown that image enhancement techniques can alleviate the limitations of datasets on model performance. Existing image enhancement algorithms mainly perform in the same category and generate highly correlated samples. Directly using authentic images to expand the dataset, the environmental noise in the image will seriously affect the model’s accuracy. Hence, this paper designs an automatic leaf segmentation algorithm (AISG) based on the EISeg segmentation method, separating the leaf information with disease spot characteristics from the background noise in the picture. This algorithm enhances the network model’s ability to extract disease features. In addition, the Cycle-GAN network is used for minor sample data enhancement to realize cross-category image transformation. Then, MobileNet was trained by transfer learning on an enhanced dataset. The experimental results reveal that the proposed method achieves a classification accuracy of 98.61% for the ten types of tomato diseases, surpassing the performance of other existing methods. Our method is beneficial in solving the problems of low accuracy and insufficient training data in tomato disease detection. This method can also provide a reference for the detection of other types of plant diseases.

Design and Research on Identification of Typical Tea Plant Diseases Using Small Sample Learning

Tea plants are susceptible to diseases during their growth. These diseases seriously affect the yield and quality of tea. The effective prevention and control of diseases requires accurate identification of diseases. With the development of artificial intelligence and computer vision, automatic recognition of plant diseases using image features has become feasible. As the support vector machine (SVM) is suitable for high dimension, high noise, and small sample learning, this paper uses the support vector machine learning method to realize the segmentation of disease spots of diseased tea plants. An improved Conditional Deep Convolutional Generation Adversarial Network with Gradient Penalty (C-DCGAN-GP) was used to expand the segmentation of tea plant spots. Finally, the Visual Geometry Group 16 (VGG16) deep learning classification network was trained by the expanded tea lesion images to realize tea disease recognition.

Progresses and hot spots of cerebrovascular disease in the past decade

Progresses and hot spots of cerebrovascular disease in the past decade

A Hierarchical Feature-Aware Model for Accurate Tomato Blight Disease Spot Detection: Unet with Vision Mamba and ConvNeXt Perspective

A Hierarchical Feature-Aware Model for Accurate Tomato Blight Disease Spot Detection: Unet with Vision Mamba and ConvNeXt Perspective

Incremental RPN: Hierarchical Region Proposal Network for Apple Leaf Disease Detection in Natural Environments.

Apple leaf diseases can seriously affect apple production and quality, and accurately detecting them can improve the efficiency of disease monitoring. Owing to the complex natural growth environment, apple leaf lesions may be easily confused with background noise, leading to poor performance. In this study, a cascaded Incremental Region Proposal Network (Inc-RPN) is proposed to accurately detect apple leaf diseases in natural environments. The proposed Inc-RPN has a two-layer RPN architecture, where the precursor RPN is leveraged to generate diseased leaf proposals, and the successor RPN focuses on extracting target disease spots based on diseased leaf proposals. In the successor RPN, a low-level feature aggregation module is designed to fully utilize the bridged features and preserve the semantic information of the target disease spots. An incremental module is also leveraged to extract aggregated diseased leaf features and target disease spot features. Finally, a novel position anchor generator is designed to generate anchors based on diseased leaf proposals. The experimental results show that the proposed Inc-RPN performs very well on the FALD_CED and Apple Leaf Disease datasets, showing that it can accurately perform apple leaf disease detection tasks.

Research on a Method for Identification of Peanut Pests and Diseases Based on a Lightweight LSCDNet Model.

Timely and accurate identification of peanut pests and diseases, coupled with effective countermeasures, is pivotal for ensuring high-quality and efficient peanut production. Despite the prevalence of pests and diseases in peanut cultivation, challenges such as minute disease spots, the elusive nature of pests, and intricate environmental conditions often lead to diminished identification accuracy and efficiency. Moreover, continuous monitoring of peanut health in real-world agricultural settings demands solutions that are computationally efficient. Traditional deep learning models often require substantial computational resources, limiting their practical applicability. In response to these challenges, we introduce LSCDNet (Lightweight Sandglass and Coordinate Attention Network), a streamlined model derived from DenseNet. LSCDNet preserves only the transition layers to reduce feature map dimensionality, simplifying the model's complexity. The inclusion of a sandglass block bolsters features extraction capabilities, mitigating potential information loss due to dimensionality reduction. Additionally, the incorporation of coordinate attention addresses issues related to positional information loss during feature extraction. Experimental results showcase that LSCDNet achieved impressive metrics with accuracy, precision, recall, and Fl score of 96.67, 98.05, 95.56, and 96.79%, respectively, while maintaining a compact parameter count of merely 0.59 million. When compared with established models such as MobileNetV1, MobileNetV2, NASNetMobile, DenseNet-121, InceptionV3, and X-ception, LSCDNet outperformed with accuracy gains of 2.65, 4.87, 8.71, 5.04, 6.32, and 8.2%, respectively, accompanied by substantially fewer parameters. Lastly, we deployed the LSCDNet model on Raspberry Pi for practical testing and application and achieved an average recognition accuracy of 85.36%, thereby meeting real-world operational requirements.

Transcriptome and Metabolome Analyses Reveal Response Mechanisms to Alternaria brassicicola-Induced Black Spot Disease in Diverse Chinese Cabbage Genotypes

Chinese cabbage (Brassica rapa L. ssp. pekinensis) is an important food crop. However, its growth and development are commonly impacted by black spot disease. To examine the response mechanisms of Chinese cabbage to black spot disease, transcriptome and metabolome sequencing were performed on the leaves of Chinese cabbage genotypes J405 (resistant) and B214 (susceptible), 48 h post-infection (hpi) with Alternaria brassicicola. Expression of essential genes in the jasmonic acid, cytokinin, and auxin signaling pathways of both Chinese cabbage genotypes was inhibited. The expression of the pathogenesis-related protein 1 (PR1) gene mediated by the salicylic acid pathway is inhibited in the Chinese cabbage genotype B214. The basic endochitase B (CHIB) gene in the ethylene pathway of both Chinese cabbage genotypes was upregulated. The accumulation of reactive oxygen species in the disease spots of Chinese cabbage genotype J405 was greater than in genotype B214. The respiratory burst oxidase (RBOH) gene in the reactive oxygen species metabolic pathway was significantly upregulated in genotype J405, while no change was observed in genotype B214. We found that oxidation-reduction-related genes such as type-2 peroxiredoxin genes, NADPH-dependent thioredoxin reductase genes, glutathione peroxidase genes, and glutathione S-transfer genes were differentially expressed across both Chinese cabbage genotypes at 48 hpi. Metabolomics demonstrated that delta-tocopherol and S-hexyl glutathione were all downregulated in genotype J405, while they were upregulated in genotype B214. This approach also identified differential expression of genes in the carotenoid biosynthesis pathway, the glycinebetaine biosynthesis pathway, as well as in the specific sulfur glycoside metabolism pathway. These findings indicate that ethylene signaling is important in the hormone signaling regulatory network-mediated disease resistance and defense in Chinese cabbage. When facing pathogen infection, hormone transduction pathways associated with growth and development in Chinese cabbage are inhibited. The accumulation of reactive oxygen species and the outbreak of various secondary metabolites may endow the Chinese cabbage genotype J405 with increased resistance to black spot disease.

Macroalgal Diseases: Exploring Biology, Pathogenesis, and Management Strategies

The global seaweed market is expected to reach USD 17.8 billion by 2032, fuelled by growing demand for sustainable and healthy food solutions and expanding applications in agriculture and aquaculture. However, this rapid growth poses significant challenges, particularly in managing diseases that often establish themselves in intensive macroalgal culture facilities. Red rot disease, Olpidiopsis, and green spot disease often affect marine macroalgae species of high commercial interest, as seen in Pyropia/Porphyra as has already happened for “ice-ice” malaise on Kappaphycus, causing huge economic losses. These diseases are caused by infectious agents that find their place in extreme environmental conditions, such as those characterized by sudden changes in temperature and pollution. Despite technological advances aimed at monitoring the well-being of cultivated seaweed, discrepancies between regions’ technological capabilities and species vulnerability exacerbate management difficulties. This review provides an overview of diseases prevalent among marine algae, their impact on aquaculture, and the effectiveness of currently adopted treatments. This study highlights the need to improve disease management strategies and highlights the importance of understanding host–pathogen interactions in order to mitigate future epidemics.

EFS-Former: An Efficient Network for Fruit Tree Leaf Disease Segmentation and Severity Assessment

Fruit is a major source of vitamins, minerals, and dietary fiber in people’s daily lives. Leaf diseases caused by climate change and other factors have significantly reduced fruit production. Deep learning methods for segmenting leaf diseases can effectively mitigate this issue. However, challenges such as leaf folding, jaggedness, and light shading make edge feature extraction difficult, affecting segmentation accuracy. To address these problems, this paper proposes a method based on EFS-Former. The expanded local detail (ELD) module extends the model’s receptive field by expanding the convolution, better handling fine spots and effectively reducing information loss. H-attention reduces computational redundancy by superimposing multi-layer convolutions, significantly improving feature filtering. The parallel fusion architecture effectively utilizes the different feature extraction intervals of the convolutional neural network (CNN) and Transformer encoders, achieving comprehensive feature extraction and effectively fusing detailed and semantic information in the channel and spatial dimensions within the feature fusion module (FFM). Experiments show that, compared to DeepLabV3+, this method achieves 10.78%, 9.51%, 0.72%, and 8.00% higher scores for mean intersection over union (mIoU), mean pixel accuracy (mPA), accuracy (Acc), and F_score, respectively, while having 1.78 M fewer total parameters and 0.32 G lower floating point operations per second (FLOPS). Additionally, it effectively calculates the ratio of leaf area occupied by spots. This method is also effective in calculating the disease period by analyzing the ratio of leaf area occupied by diseased spots. The method’s overall performance is evaluated using mIoU, mPA, Acc, and F_score metrics, achieving 88.60%, 93.49%, 98.60%, and 95.90%, respectively. In summary, this study offers an efficient and accurate method for fruit tree leaf spot segmentation, providing a solid foundation for the precise analysis of fruit tree leaves and spots, and supporting smart agriculture for precision pesticide spraying.

Optimization of cultivation system for Bacillus Siamese strain and its biocontrol effect on postharvest Colletotrichum gloeosporioides

In this study, we established an effective cultivation system for Bacillus Siamese (N-1), identified key antibacterial substances, and explored the underlying mechanisms of optimized N-1 fermentation for controlling pathogens. Our results demonstrate that the optimized system significantly enhanced the inhibitory effect of N-1 on five types of pathogens affecting tropical fruits. Additionally, non-targeted metabolomics analysis and ultra-high performance liquid chromatography triple quadrupole mass spectrometer system (UPLC-MS/MS) revealed the presence of lipopeptides, including Surfactin and FengycinA, in the optimized fermentation. Furthermore, in vitro experiments indicated that lipopeptide treatment (LT) at a concentration of 3.125 mg mL−1 significantly distorted the hyphae, decreased the activity of antioxidant enzymes, increased the levels of reactive oxygen species (ROS), malondialdehyde (MDA) and hydrogen peroxide (H2O2) contents, and enhanced protein exosmosis rate of C. gloeosporioides. Moreover, LT markedly reduced the activities of energy metabolism-related enzymes, including succinate dehydrogenase (SDH), Na+K+-ATPase, and Ca2+Mg2+-ATPase, as well as ATP content, leading to apoptosis of C. gloeosporioides. In vivo experiments revealed that LT significantly inhibited the expansion of disease spots caused by C. gloeosporioides in mango fruit. These findings suggest that the established culture system could serve as a potential strategy to enhance biocontrol efficacy and prevent fruit decay.

A novel antifungal peptide, SP1.2, from Rhodopseudomonas palustris against the rice blast pathogen.

Rice blast has a significant detrimental impact on rice yields, so developing efficient biological control technologies is an effective means for rice blast prevention and control. The GroEL protein has proven to be effective at preventing and managing the pathogenicity of rice blast. Here, we analyzed the amino acid sequence of the GroEL protein and synthesized the '60 kDa chaperonin signature' (350-373 amino acids) peptide SP1.2, which has potent antifungal activity. Notably, the SP1.2 peptide exhibited potent fungicidal activity against Magnaporthe oryzae, effectively inhibiting appressorium germination. Electron microscopy revealed that SP1.2 disrupted the fungal plasma membrane and bound to multiple bioactive phosphoinositides in vitro, triggering the production of reactive oxygen species. Furthermore, it also caused an increase in the acetylation of M. oryzae and induced autophagy in cells. The spray application of SP1.2 significantly reduced the number of disease spots caused by the fungal pathogen M. oryzae in rice, enhancing the defense response of rice plants. Field trials showed that the control effect was 64.59% after spraying SP1.2. Our study illustrates the antifungal activity of the structurally unique SP1.2 peptide against plant fungal pathogens and paves the way for the future development of this class of peptides as antifungal agents. © 2024 Society of Chemical Industry.

Detection of fruit tree diseases in natural environments: A novel approach based on stereo camera and deep learning

The occurrence of diseases in orchards has a significant impact on fruit yield and quality. Inspection devices equipped with cameras can effectively replace manual intervention in the process of orchard management by swiftly detecting diseases. However, the images captured by such devices often exhibit a wide vision field and contain a significant amount of extraneous information. This paper presented a method for detecting diseases in natural environments based on binocular cameras and deep learning techniques applied to fruit tree leaf images with a wide visual field. Firstly, the ZED2i binocular camera was utilized to capture image pairs from a long distance, simulating the visual field of an inspection device. These image pairs were then processed using the Unimatch stereo matching algorithm to obtain a disparity map and calculated the corresponding depth map. The depth information was used to create a mask, eliminating irrelevant background information from the images. Secondly, a lightweight disease detection (LDD) model was proposed based on the advanced YOLOv5 framework for detecting pear rust and plum perforation diseases. The backbone network consisted of shuffle channel block, inverted shuffle channel block, and convolutional block attention module, with only one detection head used in the classifier part. The final experiments evaluated the segmentation, model improvement, and disease spots detection performance. The results showed that the depth map obtained using Unimatch for stereo matching was more accurate than that obtained using the ZED software development kit. In ablation experiments, LDD achieved a mean average precision of 93.0%, with a model size of only 3.9 MB, outperforming the original YOLOv5-s model. Preprocessed images with depth information exhibited improved detection performance, achieved a F1 score of 93.62%, which was a 10.92% improvement over direct detection of the original images. Overall, the presented method successfully addresses the issue of background interference when detecting diseases of fruit tree leaf with a wide visual field, providing a technical basis for automated orchard inspection operations.

Characteristics of Corynespora cassiicola, the causal agent of tobacco Corynespora leaf spot, revealed by genomic and metabolic phenomic analysis

Corynespora cassiicola is a highly diverse fungal pathogen that commonly occurs in tropical, subtropical, and greenhouse environments worldwide. In this study, the isolates were identified as C. cassiicola, and the optimum growth and sporulation were studied. The phenotypic characteristics of C. cassiicola, concerning 950 different growth conditions, were tested using Biolog PM plates 1–10. In addition, the strain of C. cassiicola DWZ from tobacco hosts was sequenced for the using Illumina PE150 and Pacbio technologies. The host resistance of tobacco Yunyan 87 with different maturity levels was investigated. In addition, the resistance evaluation of 10 common tobacco varieties was investigated. The results showed that C. cassiicola metabolized 89.47% of the tested carbon source, 100% of the nitrogen source, 100% of the phosphorus source, and 97.14% of the sulfur source. It can adapt to a variety of different osmotic pressure and pH environments, and has good decarboxylase and deaminase activities. The optimum conditions for pathogen growth and sporulation were 25–30 °C, and the growth was better on AEA and OA medium. The total length of the genome was 45.9 Mbp, the GC content was 51.23%, and a total of 13,061 protein-coding genes, 202 non-coding RNAs and 2801 and repeat sequences were predicted. Mature leaves were more susceptible than proper mature and immature leaves, and the average diameter of diseased spots reached 17.74 mm at 12 days. None of the tested ten cultivars exhibited obvious resistance to Corynespora leaf spot of tobacco, whereby all disease spot diameters reached > 10 mm and > 30 mm when at 5 and 10 days after inoculation, respectively. The phenotypic characteristics, genomic analysis of C. cassiicola and the cultivar resistance assessment of this pathogen have increased our understanding of Corynespora leaf spot of tobacco.

First report of Fusarium humuli causing stem spots in Nicotiana tabacum L in China.

Tobacco is one of the most important economic crops in China. Disease is one of the main factors affecting the quality of tobacco production (Cai et al. 2022). Stem spot disease of tobacco was observed in the Planting Demonstration Garden in Chang Ning (26°37N; 112° 31E), Hunan Province of China, from May to June 2023. The disease seriously retarded tobacco growth and the incidence rate was about 30-50% of the plants(Yun Yan 87). Most of infected tobacco had black spots on the stems, and the spots expanded and joined together quickly, while many stems turned black and withered. For pathogen isolation, symptomatic stem samples were collected and disinfected with 75% ethanol for 30 s and 2% sodium hypochlorite for 1 minute, followed by rinsing with sterile distilled water three times. Subsequently, small pieces (5 × 5 mm) of diseased tissues were placed on potato dextrose agar (PDA) and incubated in the dark at 25 °C for 24 h to 36 h. The emerging fungal hyphal tips were transferred to PDA and purified by the single-spore method(Yu et al. 2022). In total, 16 cultures with the same appearance were isolated from 30 disease spots on the stem. Strain coded as hnxryc2 was randomly selected for identification. After culturing in PDA for 7 days, white and dense colonies wereobserved with a mean radial growth rate of 6.4 mm/day. The strain cultured 10 days on SNA. Morphological observations were made on 10-day-old culture on SNA medium, and macroconidia were sickle-shaped and slightly curved, with 3-5 septa (2.32-7.00 µm × 0.53-1.17 µm, n = 50), neither microconidia nor chlamydospores were observed. These morphological characteristics were consistent with the description of Fusarium humuli (Wang et al. 2019, Li et al. 2023). Furthermore, primers ITS1/ITS4, EF728F/EF986R, RPB1-F5/RPB1-R8 and fRPB2-5F2/fRPB2-7cR(Xie et al. 2023) were used to amplify the ITS region, EF-1α, RPB1, and RPB2 from strain hnxryc2, respectively. The sequence alignment of hnxryc2 with the NCBI database and FUSARIOID-ID shows the following results: The sequence of ITS region(GenBank accession number PP543715) was 100% identical to these of Fusarium sp. (MN428026.1), the sequences of EF-1α, RPB1, and RPB2 of strain hnxryc2(GenBank accession numbersOR257586, OR326856 and OR257587 respectively) were 99% to 100% identical to these of F. humuli (GenBank accession numbers MK289578.1, MZ824672.1 and MZ824673.1, respectively). Then a phylogenetic tree based on ITS region, EF-1α, and RPB2 sequences was constructed (Kroon et al. 2004). The strain hnxryc2 was more closely related to F. humuli (CGMCC3.19374 GenBank accession nos. MK280845.1, MK289570.1 and MK289724.1, respectively), with bootstrap values 88%. Pathogenicity tests were performed on detached stems of tobacco and potted plants. Wounded stems were inoculated with conidial suspensions (100 μL, 1×107 spores/mL), and the controls were inoculated with sterile water (Xu et al. 2023). The inoculated detached stems were kept in humid chambers (Zhong et al., 2019), each treatment was given a 12h/12h light/dark cycle at 25°C. Deep black spots were observed for 3 days after inoculation. After 9 days, typical symptoms similar to the original diseased plants in the field were found on all inoculated stems, while the control stems did not exhibit any symptoms. Pathogenicity assays were repeated thrice. The pathogen F. humuli was successfully reisolated from the stem of inoculated samples showing symptoms. To our knowledge, this is the first report of F. humuli inducing stem spot on tobacco in China. Since F. humuli is a common pathogenic fungus that infects different plant species, more attention should be paid to its prevalence in tobacco, and the potential risk of a disease outbreak in other provinces of China.

Inhibition of the Growth of Escherichia coli and Staphylococcus aureus Microorganisms in Aesthetic Orthodontic Brackets through the In Situ Synthesis of Ag, TiO2 and Ag/TiO2 Nanoparticles.

Plaque control is especially important during orthodontic treatment because areas of the teeth near brackets and wires are difficult to clean with a toothbrush, resulting in debris buildup of food or dental plaque, thus causing caries and periodontal disease. The objective of this study was to evaluate the antimicrobial properties of silver nanoparticles (AgNPs), titanium dioxide nanoparticles (TiO2NPs), and silver/titanium dioxide nanoparticles (Ag/TiO2NPs), synthesized on the surface of α-alumina ceramic brackets. The AgNPs and TiO2NPs were synthesized by a simple chemical method, and these were characterized by XRD, SEM, and XPS TEM; the antimicrobial activity was tested against Staphylococcus aureus and Escherichia coli by diffusion test. The results of this study demonstrated that by this simple chemical method, silver and titanium dioxide nanoparticles can be synthesized on the surface of α-alumina esthetic brackets, and these NPs possess good antimicrobial activity and the possibility of reducing dental caries, periodontal disease, and white spot generated during orthodontic treatment.

VmSom1 is essential for growth, development, maintenance of cell wall integrity and virulence in Valsa mali

Apple Valsa canker disease, caused by Valsa mali Miyabe et Yamada, severely endangers the healthy growth of apple trees. The Som1, located downstream of the cyclic AMP-dependent protein kinase A (cAMP-PKA) pathway, plays crucial roles in the growth, development, morphological differentiation, and virulence of filamentous fungi. In this study, we identify and functionally characterize VmSom1, a homolog of Som1, in Valsa mali. The VmSom1 gene is located on chromosome 12, encoding an 824 amino acid protein. Phylogenetic analysis reveals VmSom1 as a fungal Som1 homolog. The VmSom1 deletion mutants exhibit slower growth rates and fail to produce pycnidia. Additionally, their hyphal growth is significantly inhibited on media containing Calcofluor White, Congo Red, NaCl, and sorbitol. The growth rate of VmSom1 deletion mutants is reduced on maltose, lactose, sucrose and fructose media but increases on glucose medium. Moreover, the mycelial growth rate of the VmSom1 deletion mutant is significantly lower than that of the wild-type strain in peptone, NH4SO4, NaNO3, and no nitrogen. Notably, the distances between the septa increase, and chitin concentration shifts to the hyphal tip in the VmSom1 deletion mutant. Furthermore, compared with the wild-type strain, the VmSom1 deletion mutant exhibits fewer diseased spots on apple fruit and branches. Overall, our findings demonstrate that VmSom1 is involved in regulating the growth and development, colony surface hydrophobicity, osmotic stress, cell wall integrity maintenance, carbon and nitrogen source utilization, septa formation, and virulence of V. mali.

An improved YOLOv5-based apple leaf disease detection method

The effective identification of fruit tree leaf disease is of great practical significance to reduce pesticide spraying, improve fruit yield and realize ecological agriculture. Computer vision technology can be effectively identifying and prevent plant diseases and insect pests. However, the lack of consideration of disease diversity and accuracy of existing detection models hinders their application and development in the field of plant pest detection. This paper proposes an efficient detection model of apple leaf disease spot through the improvement of the traditional Yolov5 detection network called A-Net. In order to significantly increase the A-Net's detection speed and accuracy, the A-Net model applies the loss function Wise-IoU, which includes the attention mechanism and the dynamic focusing mechanism, to the Yolov5 network model. The RepVGG module is then used to replace the original model's convolution module. The experimental results show that the improved model effectively suppresses the growth of some error weights. Compared with several object detection models, the improved A-Net model has a Mean Average Precision across IoU threshold 0.5 and an accuracy of 92.7%, which fully proves that the improved A-Net model has more advantages in detecting apple leaf diseases.