DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification

Tomato is an important and fragile crop. During the course of its development, it is frequently contaminated with bacteria or viruses. Tomato leaf diseases may be detected quickly and accurately, resulting in increased productivity and quality. Because of the intricate development environment of tomatoes and their inconspicuous disease spot features and small spot area, present machine vision approaches fail to reliably recognize tomato leaves. As a result, this research proposes a novel paradigm for detecting tomato leaf disease. The INLM (integration nonlocal means) filtering algorithm, for example, decreases the interference of surrounding noise on the features. Then, utilizing ResNeXt50 as the backbone, we create DCCAM-MRNet, a novel tomato image recognition network. Dilated Convolution (DC) was employed in STAGE 1 of the DCCAM-MRNet to extend the network\u27s perceptual area and locate the scattered disease spots on tomato leaves. The coordinate attention (CA) mechanism is then introduced to record cross-channel information and direction- and position-sensitive data, allowing the network to more accurately detect localized tomato disease spots. Finally, we offer a mixed residual connection (MRC) technique that combines residual block (RS-Block) and transformed residual block (TR-Block) (TRS-Block). This strategy can increase the network\u27s accuracy while also reducing its size. The DCCAM-classification MRNet\u27s accuracy is 94.3 percent, which is higher than the existing network, and the number of parameters is 0.11 M lesser than the backbone network ResNeXt50, according to the experimental results. As a result, combining INLM and DCCAM-MRNet to identify tomato diseases is a successful strategy

DS-MENet for the Classification of Citrus Disease

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life

DS-MENet for the Classification of Citrus Disease

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life

Comparative analyses of Linderniaceae plastomes, with implications for its phylogeny and evolution

IntroductionThe recently established Linderniaceae, separated from the traditionally defined Scrophulariaceae, is a taxonomically complicated family. Although previous phylogenetic studies based on a few short DNA markers have made great contributions to the taxonomy of Linderniaceae, limited sampling and low resolution of the phylogenetic tree have failed to resolve controversies between some generic circumscriptions. The plastid genome exhibits a powerful ability to solve phylogenetic relationships ranging from shallow to deep taxonomic levels. To date, no plastid phylogenomic studies have been carried out in Linderniaceae.MethodsIn this study, we newly sequenced 26 plastid genomes of Linderniaceae, including eight genera and 25 species, to explore the phylogenetic relationships and genome evolution of the family through plastid phylogenomic and comparative genomic analyses.ResultsThe plastid genome size of Linderniaceae ranged from 152,386 bp to 154,402 bp, exhibiting a typical quartile structure. All plastomes encoded 114 unique genes, comprising 80 protein-coding genes, 30 tRNA genes, and four rRNA genes. The inverted repeat regions were more conserved compared with the single-copy regions. A total of 1803 microsatellites and 1909 long sequence repeats were identified, and five hypervariable regions (petN-psbM, rps16-trnQ, rpl32-trnL, rpl32, and ycf1) were screened out. Most protein-coding genes were relatively conserved, with only the ycf2 gene found under positive selection in a few species. Phylogenomic analyses confirmed that Linderniaceae was a distinctive lineage and revealed that the presently circumscribed Vandellia and Torenia were non-monophyletic.DiscussionComparative analyses showed the Linderniaceae plastomes were highly conservative in terms of structure, gene order, and gene content. Combining morphological and molecular evidence, we supported the newly established Yamazakia separating from Vandellia and the monotypic Picria as a separate genus. These findings provide further evidence to recognize the phylogenetic relationships among Linderniaceae and new insights into the evolution of the plastid genomes

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

Evaluating mechanism and inconsistencies in hydraulic conductivity of unsaturated soil using newly proposed biochar conductivity factor

In the past few decades, numerous studies have been conducted to promote the use of biochar as a soil amendment and most recently, for compacted geo-engineered soils. In general, the definite trends of biochar effects on water retention and fertility of soils have been confirmed. However, the biochar effects on hydraulic conductivity, particularly unsaturated hydraulic conductivity of soil-biochar mix remain unclear, making it difficult to understand water seepage in both agricultural and geo-engineered infrastructures in semi-arid regions. This study examines the unsaturated hydraulic conductivity function derived based on the measurements of soil water characteristic curves of soil with biochar contents of 0%, 5% and 10%. A new parameter “biochar conductivity factor (BCF)” is proposed to evaluate the inconsistency in reported biochar effects on soil hydraulic conductivity and to interpret it from various mechanisms (inter- and intra- pore space filling, cracking, aggregation, bio-film formation and piping/internal erosion). The impact of biochar content on unsaturated hydraulic conductivity appears to reduce as the soil becomes drier with minimal effect in residual zone. Qualitative comparison of near-saturated hydraulic conductivity with test results in the literature showed that the BCF is generally higher for smaller ratio of sand to fine content (clay and silt). Moreover, the particle size of biochar may have significant influence on soil permeability. Future scope of research has been highlighted with respect to biochar production for its applications in agriculture and geo-environmental engineering. Long term effects such as root decay and growth, aggregation and nutrient supply need to be considered. Graphical Abstract

Genome Sequencing and Comparative Transcriptomics of the Model Entomopathogenic Fungi Metarhizium anisopliae and M. acridum

Metarhizium spp. are being used as environmentally friendly alternatives to chemical insecticides, as model systems for studying insect-fungus interactions, and as a resource of genes for biotechnology. We present a comparative analysis of the genome sequences of the broad-spectrum insect pathogen Metarhizium anisopliae and the acridid-specific M. acridum. Whole-genome analyses indicate that the genome structures of these two species are highly syntenic and suggest that the genus Metarhizium evolved from plant endophytes or pathogens. Both M. anisopliae and M. acridum have a strikingly larger proportion of genes encoding secreted proteins than other fungi, while ∼30% of these have no functionally characterized homologs, suggesting hitherto unsuspected interactions between fungal pathogens and insects. The analysis of transposase genes provided evidence of repeat-induced point mutations occurring in M. acridum but not in M. anisopliae. With the help of pathogen-host interaction gene database, ∼16% of Metarhizium genes were identified that are similar to experimentally verified genes involved in pathogenicity in other fungi, particularly plant pathogens. However, relative to M. acridum, M. anisopliae has evolved with many expanded gene families of proteases, chitinases, cytochrome P450s, polyketide synthases, and nonribosomal peptide synthetases for cuticle-degradation, detoxification, and toxin biosynthesis that may facilitate its ability to adapt to heterogenous environments. Transcriptional analysis of both fungi during early infection processes provided further insights into the genes and pathways involved in infectivity and specificity. Of particular note, M. acridum transcribed distinct G-protein coupled receptors on cuticles from locusts (the natural hosts) and cockroaches, whereas M. anisopliae transcribed the same receptor on both hosts. This study will facilitate the identification of virulence genes and the development of improved biocontrol strains with customized properties

The Application of a Socio-economic Model System for Activity-Based Modeling: Experience from Southern California

At the time of publication R.M. Pendyala, K.C. Konduri, and K.P. Christian were at Arizona State University, C.R. Bhat, R. Paleti, and R. Sidharthan were at the University of Texas at Austin, K.G. Goulias was at the University of California Santa Barbara, and H. Hu and G. Huang were at Southern California Association of Governments.This paper presents results from the application of a comprehensive socio-economic and demographic model system performed in conjunction with the development of a continuous time activity-based microsimulation model of travel demand for the Southern California Association of Governments. The socio-economic model system includes two major components. The first is a synthetic population generator that is capable of synthesizing a representative population for the entire region while controlling for both household and person level marginal distributions. The second is an econometric microsimulator that models various socio-economic and demographic attributes for each person in the synthetic population with a view to develop a rich set of input data for the activity-based microsimulation model system. The results show that the socio-economic model system is capable of replicating known distributions of demographic attributes in the population and can be easily scaled for implementation in large regions such as the Southern California area that includes a population of more than 18 million people in its model boundaries.Civil, Architectural, and Environmental Engineerin

The complete chloroplast genome sequence of Disanthus cercidifolius Subsp. Longipes (Hamamelidaceae)

Disanthus cercidifolius Maximowicz subsp. longipes (H. T. Chang) K. Y. Pan is a rare and Endangered species endemic to central China. Here, we assembled and annotated the complete chloroplast (cp) genome. It is 158,148 bp in length and encodes 87 protein-coding genes, 37 transfer RNA (tRNA) genes, and 8 ribosomal RNA (rRNA) genes. This chloroplast genome sequencing offers a useful resource for future conservation genetics and phylogenetic studies