Improved EfficientNet for corn disease identification

IntroductionCorn is one of the world's essential crops, and the presence of corn diseases significantly affects both the yield and quality of corn. Accurate identification of corn diseases in real time is crucial to increasing crop yield and improving farmers' income. However, in real-world environments, the complexity of the background, irregularity of the disease region, large intraclass variation, and small interclass variation make it difficult for most convolutional neural network models to achieve disease recognition under such conditions. Additionally, the low accuracy of existing lightweight models forces farmers to compromise between accuracy and real-time.MethodsTo address these challenges, we propose FCA-EfficientNet. Building upon EfficientNet, the fully-convolution-based coordinate attention module allows the network to acquire spatial information through convolutional structures. This enhances the network's ability to focus on disease regions while mitigating interference from complex backgrounds. Furthermore, the adaptive fusion module is employed to fuse image information from different scales, reducing interference from the background in disease recognition. Finally, through multiple experiments, we have determined the network structure that achieves optimal performance.ResultsCompared to other widely used deep learning models, this proposed model exhibits outstanding performance in terms of accuracy, precision, recall, and F1 score. Furthermore, the model has a parameter count of 3.44M and Flops of 339.74M, which is lower than most lightweight network models. We designed and implemented a corn disease recognition application and deployed the model on an Android device with an average recognition speed of 92.88ms, which meets the user's needs.DiscussionOverall, our model can accurately identify corn diseases in realistic environments, contributing to timely and effective disease prevention and control

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases

Microbiome definition re-visited: old concepts and new challenges

peer-reviewedAbstract The field of microbiome research has evolved rapidly over the past few decades and has become a topic of great scientific and public interest. As a result of this rapid growth in interest covering different fields, we are lacking a clear commonly agreed definition of the term “microbiome.” Moreover, a consensus on best practices in microbiome research is missing. Recently, a panel of international experts discussed the current gaps in the frame of the European-funded MicrobiomeSupport project. The meeting brought together about 40 leaders from diverse microbiome areas, while more than a hundred experts from all over the world took part in an online survey accompanying the workshop. This article excerpts the outcomes of the workshop and the corresponding online survey embedded in a short historical introduction and future outlook. We propose a definition of microbiome based on the compact, clear, and comprehensive description of the term provided by Whipps et al. in 1988, amended with a set of novel recommendations considering the latest technological developments and research findings. We clearly separate the terms microbiome and microbiota and provide a comprehensive discussion considering the composition of microbiota, the heterogeneity and dynamics of microbiomes in time and space, the stability and resilience of microbial networks, the definition of core microbiomes, and functionally relevant keystone species as well as co-evolutionary principles of microbe-host and inter-species interactions within the microbiome. These broad definitions together with the suggested unifying concepts will help to improve standardization of microbiome studies in the future, and could be the starting point for an integrated assessment of data resulting in a more rapid transfer of knowledge from basic science into practice. Furthermore, microbiome standards are important for solving new challenges associated with anthropogenic-driven changes in the field of planetary health, for which the understanding of microbiomes might play a key role. Video Abstrac

The complete chloroplast genome of Allium wallichii Kunth (Amaryllidaceae)

Allium wallichii Kunth is a herb species with potentially extensive applications because of its edible, ornamental, and pharmaceutical values. The structural characteristics and phylogenetic relationships of its chloroplast genome were determined here for the first time. The complete cp genome was found to be 152,496 bp long, with a GC content of 37.04%. It consists of four distinct regions: a large single copy (LSC) region of 82,510 bp, a small single copy (SSC) region of 17,460 bp, and two inverted repeat (IR) regions of 26,263 bp each. The genome encodes 129 genes, including 86 protein-coding genes, 37 tRNA genes, and six rRNA genes. Our phylogenetic analysis revealed that A. wallichii was closely related to Allium wallichii var. platyphyllum, which are included in the section Bromatorrhiza, subgenus Amerallium Traub of the genus Allium. Our report provides valuable information on the genetic diversity of Allium species

Selection of Streptomyces against soil borne fungal pathogens by a standardized dual culture assay and evaluation of their effects on seed germination and plant growth

Background: In the search for new natural resources for crop protection, streptomycetes are gaining interest in agriculture as plant growth promoting bacteria and/or biological control agents. Because of their peculiar life cycle, in which the production of secondary metabolites is synchronized with the development of aerial hyphae and sporulation, the commonly used methods to screen for bacterial antagonists need to be adapted. Results: The dual culture assay was standardized in terms of inoculation timing of Streptomyces antagonist and pathogen, and growth rate of different fungal pathogens. In case of fast-growing fungi, inoculation of the antagonist 2 or 3 days prior to the pathogen resulted in significantly stronger inhibition of mycelium growth. One hundred and thirty Streptomyces strains were evaluated against six destructive soil borne pathogens. The activity of strains varied from broad-spectrum to highly specific inhibition of individual pathogens. All strains inhibited at least one tested pathogen. Three strains, which combined the largest broad-spectrum with the highest inhibition activity, were selected for further characterization with four vegetable species. All of them were able to colonize seed surface of all tested vegetable crops. They mostly improved radicle and hypocotyl growth in vitro, although no statistically significant enhancement of biomass weight was observed in vivo. Occasionally, transient negative effects on germination and plant growth were observed. Conclusions: The adapted dual culture assay allowed us to compare the inhibition of individual Streptomyces strains against six fungal soil borne pathogens. The best selected strains were able to colonize the four vegetable crops and have a potential to be developed into biocontrol products. Although they occasionally negatively influenced plant growth, these effects did not persist during the further development. Additional in vivo studies are needed to confirm their potential as biological control or plant growth promoting agents

DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification

The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Traditional Convolutional Neural Network (CNN) models have a low recognition accuracy and a large number of parameters. In this study, a lightweight corn disease identification model called DFCANet (Double Fusion block with Coordinate Attention Network) is proposed. The DFCANet consists mainly of two components: The dual feature fusion with coordinate attention and the Down-Sampling (DS) modules. The DFCA block contains dual feature fusion and Coordinate Attention (CA) modules. In order to completely fuse the shallow and deep features, these features were fused twice. The CA module suppresses the background noise and focuses on the diseased area. In addition, the DS module is used for down-sampling. It reduces the loss of information by expanding the feature channel dimension and the Depthwise convolution. The results show that DFCANet has an average recognition accuracy of 98.47%. It is more efficient at identifying corn leaf diseases in real scene images, compared with VGG16 (96.63%), ResNet50 (93.27%), EffcientNet-B0 (97.24%), ConvNeXt-B (94.18%), DenseNet121 (95.71%), MobileNet-V2 (95.41%), MobileNetv3-Large (96.33%), and ShuffleNetV2-1.0× (94.80%) methods. Moreover, the model’s Params and Flops are 1.91M and 309.1M, respectively, which are lower than heavyweight network models and most lightweight network models. In general, this study provides a novel, lightweight, and efficient convolutional neural network model for corn disease identification

Effects of Drought Stress Induced by Hypertonic Polyethylene Glycol (PEG-6000) on <i>Passiflora edulis</i> Sims Physiological Properties

Passion fruit is known to be sensitive to drought, and in order to study the physiological and biochemical changes that occur in passion fruit seedlings under drought stress, a hypertonic polyethylene glycol (PEG) solution (5%, 10%, 15%, and 20%) was used to simulate drought stress in passion fruit seedlings. We explored the physiological changes in passion fruit seedlings under drought stress induced by PEG to elucidate their response to drought stress and provide a theoretical basis for drought-resistant cultivation of passion fruit seedlings. The results show that drought stress induced by PEG had a significant effect on the growth and physiological indices of passion fruit. Drought stress significantly decreased fresh weight, chlorophyll content, and root vitality. Conversely, the contents of soluble protein (SP), proline (Pro), and malondialdehyde (MDA) increased gradually with the increasing PEG concentration and prolonged stress duration. After nine days, the SP, Pro and MDA contents were higher in passion fruit leaves and roots under 20% PEG treatments compared with the control. Additionally, with the increase in drought time, the activities of antioxidant enzymes such as peroxidase (POD), superoxide dismutase (SOD) and catalase (CAT) showed an increasing trend and then a decreasing trend, and they reached the highest value at the sixth day of drought stress. After rehydration, SP, Pro and MDA contents in the leaves and roots of passion fruit seedlings was reduced. Among all the stress treatments, 20% PEG had the most significant effect on passion fruit seedlings. Therefore, our study demonstrated sensitive concentrations of PEG to simulate drought stress on passion fruit and revealed the physiological adaptability of passion fruit to drought stress

DFCANet: A Novel Lightweight Convolutional Neural Network Model for Corn Disease Identification

The identification of corn leaf diseases in a real field environment faces several difficulties, such as complex background disturbances, variations and irregularities in the lesion areas, and large intra-class and small inter-class disparities. Traditional Convolutional Neural Network (CNN) models have a low recognition accuracy and a large number of parameters. In this study, a lightweight corn disease identification model called DFCANet (Double Fusion block with Coordinate Attention Network) is proposed. The DFCANet consists mainly of two components: The dual feature fusion with coordinate attention and the Down-Sampling (DS) modules. The DFCA block contains dual feature fusion and Coordinate Attention (CA) modules. In order to completely fuse the shallow and deep features, these features were fused twice. The CA module suppresses the background noise and focuses on the diseased area. In addition, the DS module is used for down-sampling. It reduces the loss of information by expanding the feature channel dimension and the Depthwise convolution. The results show that DFCANet has an average recognition accuracy of 98.47%. It is more efficient at identifying corn leaf diseases in real scene images, compared with VGG16 (96.63%), ResNet50 (93.27%), EffcientNet-B0 (97.24%), ConvNeXt-B (94.18%), DenseNet121 (95.71%), MobileNet-V2 (95.41%), MobileNetv3-Large (96.33%), and ShuffleNetV2-1.0&times; (94.80%) methods. Moreover, the model&rsquo;s Params and Flops are 1.91M and 309.1M, respectively, which are lower than heavyweight network models and most lightweight network models. In general, this study provides a novel, lightweight, and efficient convolutional neural network model for corn disease identification

The complete chloroplast genome of <i>Allium wallichii</i> Kunth (Amaryllidaceae)

Allium wallichii Kunth is a herb species with potentially extensive applications because of its edible, ornamental, and pharmaceutical values. The structural characteristics and phylogenetic relationships of its chloroplast genome were determined here for the first time. The complete cp genome was found to be 152,496 bp long, with a GC content of 37.04%. It consists of four distinct regions: a large single copy (LSC) region of 82,510 bp, a small single copy (SSC) region of 17,460 bp, and two inverted repeat (IR) regions of 26,263 bp each. The genome encodes 129 genes, including 86 protein-coding genes, 37 tRNA genes, and six rRNA genes. Our phylogenetic analysis revealed that A. wallichii was closely related to Allium wallichii var. platyphyllum, which are included in the section Bromatorrhiza, subgenus Amerallium Traub of the genus Allium. Our report provides valuable information on the genetic diversity of Allium species.</p