Prompt plant disease detection is critical to prevent plagues and to mitigate their effects on crops. The most accurate automatic algorithms for plant disease identification using plant field images ...are based on deep learning. These methods require the acquisition and annotation of large image datasets, which is frequently technically or economically unfeasible. This study introduces Few-Shot Learning (FSL) algorithms for plant leaf classification using deep learning with small datasets.
For the study 54,303 labeled images from the PlantVillage dataset were used, comprising 38 plant leaf and/or disease types (classes). The data was split into a source (32 classes) and a target (6 classes) domain. The Inception V3 network was fine-tuned in the source domain to learn general plant leaf characteristics. This knowledge was transferred to the target domain to learn new leaf types from few images. FSL using Siamese networks and Triplet loss was used and compared to classical fine-tuning transfer learning. The source and target domain sets were split into a training set (80%) to develop the methods and a test set (20%) to obtain the results. Algorithm performance was evaluated using the total accuracy, and the precision and recall per class. For the FSL experiments the algorithms were trained with different numbers of images per class and the experiments were repeated 20 times to statistically characterize the results.
The accuracy in the source domain was 91.4% (32 classes), with a median precision/recall per class of 93.8%/92.6%. The accuracy in the target domain was 94.0% (6 classes) learning from all the training data, and the median accuracy (90% confidence interval) learning from 1 image per class was 55.5 (46.0–61.7)%. Median accuracies of 80.0 (76.4–86.5)% and 90.0 (86.1–94.2)% were reached for 15 and 80 images per class, yielding a reduction of 89.1% (80 images/class) in the training dataset with only a 4-point loss in accuracy. The FSL method outperformed the classical fine tuning transfer learning which had accuracies of 18.0 (16.0–24.0)% and 72.0 (68.0–77.3)% for 1 and 80 images per class, respectively.
It is possible to learn new plant leaf and disease types with very small datasets using deep learning Siamese networks with Triplet loss, achieving almost a 90% reduction in training data needs and outperforming classical learning techniques for small training sets.
•Convolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment.•These methods require the acquisition and annotation of large image datasets, which is frequently technically or economically unfeasible.•This study introduces few shot learning algorithms for plant leave classification using deep learning with small datasets.•Using deep learning siamese networks with triplet loss it is possible to learn new plant leave and disease types with very small datasets, achieving almost a 90% reduction in training data needs and outperforming classical learning techniques for small training sets.
Plant pathogens are a significant challenge in agriculture despite our best efforts to combat them. One of the most effective and sustainable ways to manage plant pathogens is to use genetic ...modification (GM) and genome editing, expanding the breeder’s toolkit. For use in the field, these solutions must be efficacious, with no negative effect on plant agronomy, and deployed thoughtfully. They must also not introduce a potential allergen or toxin. Expensive regulation of biotech crops is prohibitive for local solutions. With 11–30% average global yield losses and greater local impacts, tackling plant pathogens is an ethical imperative. We need to increase world food production by at least 60% using the same amount of land, by 2050. The time to act is now and we cannot afford to ignore the new solutions that GM provides to manage plant pathogens.
ABSTRACT
Xanthomonas is a well-studied genus of bacterial plant pathogens whose members cause a variety of diseases in economically important crops worldwide. Genomic and functional studies of these ...phytopathogens have provided significant understanding of microbial-host interactions, bacterial virulence and host adaptation mechanisms including microbial ecology and epidemiology. In addition, several strains of Xanthomonas are important as producers of the extracellular polysaccharide, xanthan, used in the food and pharmaceutical industries. This polymer has also been implicated in several phases of the bacterial disease cycle. In this review, we summarise the current knowledge on the infection strategies and regulatory networks controlling virulence and adaptation mechanisms from Xanthomonas species and discuss the novel opportunities that this body of work has provided for disease control and plant health.
Here, we discuss the current knowledge surrounding regulatory networks and systems that control virulence and adaption mechanisms in Xanthomonas species. Additionally, we detail how study of these pathogens has provided novel opportunities for disease control and plant health.
Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially ...detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, -92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400-1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial-spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900-940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400-700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.
Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world. Now a global human pandemic is threatening the health of millions on our planet. A ...stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover, and evolution of new pathogen lineages. In order to tackle these grand challenges, a new set of tools that include disease surveillance and improved detection technologies including pathogen sensors and predictive modeling and data analytics are needed to prevent future outbreaks. Herein, we describe an integrated research agenda that could help mitigate future plant disease pandemics.
Plant Diseases and Pests are a major challenge in the agriculture sector. An accurate and a faster detection of diseases and pests in plants could help to develop an early treatment technique while ...substantially reducing economic losses. Recent developments in Deep Neural Networks have allowed researchers to drastically improve the accuracy of object detection and recognition systems. In this paper, we present a deep-learning-based approach to detect diseases and pests in tomato plants using images captured in-place by camera devices with various resolutions. Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we consider three main families of detectors: Faster Region-based Convolutional Neural Network (Faster R-CNN), Region-based Fully Convolutional Network (R-FCN), and Single Shot Multibox Detector (SSD), which for the purpose of this work are called "deep learning meta-architectures". We combine each of these meta-architectures with "deep feature extractors" such as VGG net and Residual Network (ResNet). We demonstrate the performance of deep meta-architectures and feature extractors, and additionally propose a method for local and global class annotation and data augmentation to increase the accuracy and reduce the number of false positives during training. We train and test our systems end-to-end on our large Tomato Diseases and Pests Dataset, which contains challenging images with diseases and pests, including several inter- and extra-class variations, such as infection status and location in the plant. Experimental results show that our proposed system can effectively recognize nine different types of diseases and pests, with the ability to deal with complex scenarios from a plant's surrounding area.
Co-incubation of plant growth promoting rhizobacteria (PGPRs) have been proposed as a potential alternative to pesticides for controlling fungal pathogens in crops, but their synergism mechanisms are ...not yet fully understood. In this study, combined use of Bacillus subtilis SL44 and Enterobacter hormaechei Wu15 could decrease the density of Colletotrichum gloeosporioides and Rhizoctonia solani and enhance the growth of beneficial bacteria on the mycelial surface, thereby mitigating disease severity. Meanwhile, PGPR application led to a reorganization of the rhizosphere microbial community through modulating its metabolites, such as extracellular polymeric substances and chitinase. These metabolites demonstrated positive effects on attracting and enhancing conventional periphery bacteria, inhibiting fungal pathogens and promoting soil health effectively. The improvement in the microbial community structure altered the trophic mode of soil fungal communities, effectively decreasing the proportion of saprotrophic soil and reducing fungal plant diseases. Certain combinations of PGPR have the potential to serve as precise instruments for managing plant pathogens.
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•The co-evolution of PGPR with fungal pathogen enhanced their disease resistance.•The adhesion capability of PGPR was enhanced through the co-cultivation.•PGPR mitigated the adverse impacts of fungal pathogen on soil communities.•PGPR has significantly improved the soil's ecological environment.
The role of water in plant–microbe interactions Aung, Kyaw; Jiang, Yanjuan; He, Sheng Yang
The Plant journal : for cell and molecular biology,
February 2018, Letnik:
93, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Summary
Throughout their life plants are associated with various microorganisms, including commensal, symbiotic and pathogenic microorganisms. Pathogens are genetically adapted to aggressively ...colonize and proliferate in host plants to cause disease. However, disease outbreaks occur only under permissive environmental conditions. The interplay between host, pathogen and environment is famously known as the ‘disease triangle’. Among the environmental factors, rainfall events, which often create a period of high atmospheric humidity, have repeatedly been shown to promote disease outbreaks in plants, suggesting that the availability of water is crucial for pathogenesis. During pathogen infection, water‐soaking spots are frequently observed on infected leaves as an early symptom of disease. Recent studies have shown that pathogenic bacteria dedicate specialized virulence proteins to create an aqueous habitat inside the leaf apoplast under high humidity. Water availability in the apoplastic environment, and probably other associated changes, can determine the success of potentially pathogenic microbes. These new findings reinforce the notion that the fight over water may be a major battleground between plants and pathogens. In this article, we will discuss the role of water availability in host–microbe interactions, with a focus on plant–bacterial interactions.
Significance Statement
Environmental conditions are major determinants affecting disease outbreaks in field crops. Recent studies have begun to shed light on how pathogenic bacteria utilize virulence proteins to create an aqueous habitat inside the leaf apoplast, under high‐humidity conditions, to aggressively colonize plant leaves.
•We propose a deep learning‐based method for tomato plant disease detection.•We generate synthetic images using C‐GAN for data augmentation purposes.•A DenseNet121 model is trained on the original ...tomato leaf and synthetic images.•The proposed data augmentation technique improves network generalizability.•Proposed method achieves the best accuracy of 99.51% for 5‐class classification.
Plant diseases and pernicious insects are a considerable threat in the agriculture sector. Therefore, early detection and diagnosis of these diseases are essential. The ongoing development of profound deep learning methods has greatly helped in the detection of plant diseases, granting a vigorous tool with exceptionally precise outcomes but the accuracy of deep learning models depends on the volume and the quality of labeled data for training. In this paper, we have proposed a deep learning-based method for tomato disease detection that utilizes the Conditional Generative Adversarial Network (C-GAN) to generate synthetic images of tomato plant leaves. Thereafter, a DenseNet121 model is trained on synthetic and real images using transfer learning to classify the tomato leaves images into ten categories of diseases. The proposed model has been trained and tested extensively on publicly available PlantVillage dataset. The proposed method achieved an accuracy of 99.51%, 98.65%, and 97.11% for tomato leaf image classification into 5 classes, 7 classes, and 10 classes, respectively. The proposed approach shows its superiority over the existing methodologies.
Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease ...in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases. It also covers survey on different diseases classification techniques that can be used for plant leaf disease detection. Image segmentation, which is an important aspect for disease detection in plant leaf disease, is done by using genetic algorithm.
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