In Bangladesh, sweet orange cultivation has been popular among fruit growers as the fruit is in demand. However, the disease of sweet oranges decreases fruit production. Research suggests that computer-aided disease diagnosis and machine learning (IML) models can improve fruit production by detecting and classifying diseases. In this line, a dataset of sweet oranges is required to diagnose the disease. Moreover, like many other fruits, sweet orange disease may vary from country to country. Therefore, in Bangladesh, a sweet orange dataset is required. Lastly, since different ML algorithms require datasets in various formats, only a few existing datasets fulfil the necessity. To fulfil the limitations, a sweet orange dataset in Bangladesh is collected. The dataset was collected in August and comprises high-quality images documenting multiple disease conditions, including Citrus Canker, Citrus Greening, Citrus Mealybugs, Die Back, Foliage Damage, Spiny Whitefly, Powdery Mildew, Shot Hole, Yellow Dragon, Yellow Leaves, and Healthy Leaf. These images provide an opportunity to apply machine learning and computer vision techniques to detect and classify diseases. This dataset aims to help researchers advance agri engineering through ML. Other sweet orange growing countries with having similar environments may find helpful information. Lastly, such experiments using our dataset will assist farmers in taking preventive measures and minimising economic losses.

The rhizosphere microbiome is important for plant health, yet their contributions to disease resistance and assembly dynamics remain unclear. This study employed rhizosphere microbiome transplantation (RMT) to delineate the impact of the rhizosphere microbiome and the immune response of eggplant (Solanum melongena) on resistance to bacterial wilt caused by Ralstonia solanacearum. We first identified disease-suppressive and disease-conducive rhizosphere microbiome in a susceptible tomato recipient. Using a non-destructive rhizobox and 16S rRNA amplicon sequencing, we monitored the dynamics of both microbiome types during the eggplant development. Most differences were observed at the early stage and then diminished over time. The suppressive microbiome maintained a higher proportion of initial community members throughout eggplant development and exhibited stronger deterministic processes in the early stage, underscoring the importance of plant selection in recruiting protective microbes for rhizosphere immunity. Our study sheds light on the development of microbiome-based strategies for plant disease management and resistance breeding.

CONCLUSIONS: A group of genes that were upregulated in a resistant cultivar while downregulated in a susceptible cultivar in a transcriptomics analysis of potato challenged by Spongospora subterranea infection, did not show the same expression pattern at the protein level.

Here, we report the draft genome sequence of Xanthomonas arboricola pv. pruni strain PVCT 262.1, isolated from almond (Prunus dulcis) leaves affected by bacterial spots in Italy in 2020. Genome size is 5,076,418 bp and G+C content is 65.44%. A total of 4,096 protein-coding genes and 92 RNAs are predicted.

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The anther-smut host-pathogen system has provided extensive insights into the evolutionary ecology of disease resistance, transmission modes, host shifts, pathogen specialization, and disease evolution in metapopulations. It also has led to unexpected insights into sex ratio distorters, sex chromosome evolution, and transposable elements in fungi. In addition, anther-smut disease played a major role in Linnaeus\' germ theory and the correspondence on parasitic castration between Darwin and Becker, one of the first female botanists. Here, we explicitly highlight some of the realities in the process of science, using an unusual autobiographical approach to describe how we came to collaborate on this system in the 1980s. Using perspectives from our different career stages, we present a surprising narrative that could not be deduced from merely reading the published papers. While our work was grounded in previous ecological and evolutionary theory, it was the product as much of empirical failures and intellectual roadblocks, as the result of a progressive scientific method. Our experiences illustrate not only the \"human dimension of science\" but more importantly show that linear sequences of hypothesis testing do not necessarily lead to new study systems and new ideas. We suggest there is a need to re-evaluate the scientific method in ecology and evolution, especially where the challenge is to engage in a productive dialog between natural history and theory.

Pathogens are engaged in a fierce evolutionary arms race with their host. The genes at the forefront of the engagement between kingdoms are often part of diverse and highly mutable gene families. Even in this context, we discovered unprecedented variation in the hyper-variable (HYP) effectors of plant-parasitic nematodes. HYP effectors are single-gene loci that potentially harbor thousands of alleles. Alleles vary in the organization, as well as the number, of motifs within a central hyper-variable domain (HVD). We dramatically expand the HYP repertoire of two plant-parasitic nematodes and define distinct species-specific \"rules\" underlying the apparently flawless genetic rearrangements. Finally, by analyzing the HYPs in 68 individual nematodes, we unexpectedly found that despite the huge number of alleles, most individuals are germline homozygous. These data support a mechanism of programmed genetic variation, termed HVD editing, where alterations are locus specific, strictly governed by rules, and theoretically produce thousands of variants without errors.

Plant diseases significantly impact food security and food safety. It was estimated that food production needs to increase by 50% to feed the projected 9.3 billion people by 2050. Yet, plant pathogens and pests are documented to cause up to 40% yield losses in major crops, including maize, rice, and wheat, resulting in annual worldwide economic losses of approximately US$220 billion. Yield losses due to plant diseases and pests are estimated to be 21.5% (10.1 to 28.1%) in wheat, 30.3% (24.6 to 40.9%) in rice, and 22.6% (19.5 to 41.4%) in maize. In March 2023, The American Phytopathological Society (APS) conducted a survey to identify and rank key challenges in plant pathology in the next decade. Phytopathology subsequently invited papers that address those key challenges in plant pathology, and these were published as a special issue. The key challenges identified include climate change effect on the disease triangle and outbreaks, plant disease resistance mechanisms and its applications, and specific diseases including those caused by Candidatus Liberibacter spp. and Xylella fastidiosa. Additionally, disease detection, natural and man-made disasters, and plant disease control strategies were explored in issue articles. Finally, aspects of open access and how to publish articles to maximize the Findability, Accessibility, Interoperability, and Reuse of digital assets in plant pathology were described. Only by identifying the challenges and tracking progress in developing solutions for them will we be able to resolve the issues in plant pathology and ultimately ensure plant health, food security, and food safety.

Ten years ago, (black) stem rust - the most damaging of wheat (Triticum aestivum) rusts - re-emerged in western Europe. Disease incidences have since increased in scale and frequency. Here, we investigated the likely underlying causes and used those to propose urgently needed mitigating actions. We report that the first large-scale UK outbreak of the wheat stem rust fungus, Puccinia graminis f. sp. tritici (Pgt), in 2022 may have been caused by timely arrival of airborne urediniospores from southwest Europe. The drive towards later-maturing wheat varieties in the UK may be exacerbating Pgt incidences, which could have disastrous consequences. Indeed, infection assays showed that two UK Pgt isolates from 2022 could infect over 96% of current UK wheat varieties. We determined that the temperature response data in current disease risk simulation models are outdated. Analysis of germination rates for three current UK Pgt isolates showed substantial variation in temperature response functions, suggesting that the accuracy of disease risk simulations would be substantially enhanced by incorporating data from prevailing Pgt isolates. As Pgt incidences continue to accelerate in western Europe, we advocate for urgent action to curtail Pgt losses and help safeguard future wheat production across the region.

Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in-depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.