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Dataset results
19 results for “Crop disease”
Irish Potato Imagery Dataset for Early Detection of Crop Diseases
<p>The annotated dataset consists of irish potatoes leaf imagery for early diseases detection. The irish potato crop leaves images were taken in Mbeya region in the Southern Highlands of Tanzania between 22<sup>nd</sup> November 2022 and 08<sup>th</sup> April 2023 using a mobile data collection tool, called the Open Data Kit (ODK). The crop leaf imagery dataset use case is developing machine learning models and end-user tools for early detection of (i) Early blight, and (ii) Late blight diseases in irish potatoes. The common leaf imagery data was collected from small holder farms using Samsung Galaxy A03 Core smartphones. </p> <p>All images are in the <strong>.zip files</strong>; “lateblt.zip” has 20,499 images, “healthy.zip” has 20,438 images, and “earlyblt.zip” has 17,772 images. A total of 58,709 image files are labelled.</p> <p>This research project is financially supported by the International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (SIDA) through the Artificial Intelligence for Agriculture and Food Systems Innovation Research Network (AI4AFS-IRN) administered by the African Technology Policy Studies Network (ATPS) with Grant Award Number: AI4AFS/GA/AFS-2504001568.</p>
Common Beans Imagery Dataset for Early Detection of Crop Diseases
<p>The annotated dataset consists of common beans leaf imagery for early diseases detection. The common beans crop leaves images were taken in Mbeya region in the Southern Highlands of Tanzania between 20<sup>th</sup> October 2022 and 10<sup>th</sup> April 2023 using a mobile data collection tool, called the Open Data Kit (ODK). The crop leaf imagery dataset use case is developing machine learning models and end-user tools for early detection of (i) Bean anthracnose, and (ii) Bean rust diseases in common beans. The common leaf imagery data was collected from small holder farms using Samsung Galaxy A03 Core smartphones. </p> <p>All images are in the <strong>.zip files</strong>; “anthra.zip” has 13,531 images, “healthy.zip” has 24,973 images, and “rust.zip” has 20,568 images. A total of 59,072 image files are labelled.</p> <p>This research project is financially supported by the International Development Research Centre (IDRC) and the Swedish International Development Cooperation Agency (SIDA) through the Artificial Intelligence for Agriculture and Food Systems Innovation Research Network (AI4AFS-IRN) administered by the African Technology Policy Studies Network (ATPS) with Grant Award Number: AI4AFS/GA/AFS-2504001568.</p>
Disease management during bloom affects the floral microbiome but not pollination in a mass-flowering crop
<p>Flowering crops are heavily managed during bloom to both promote pollination and prevent disease. Disease management practices can alter the floral microbiome, including pathogens and non-target microbes. However, whether agrochemical presence or altered microbiome composition affect pollinator foraging and pollination services is unclear.</p> <p>We assessed the effects of orchard management tactics and landscape context on the flower microbiome in almond, <em>Prunus dulcis</em>. Fourteen orchards (5 conventional, 4 organic, 5 conventional with habitat augmentation) were sampled at early and peak bloom to characterize bacterial and fungal communities associated with floral tissues. The surveys were complemented by an artificial flower experiment to assess the effects of fungicides and microbes on honey bee foraging. Finally, a field trial was conducted to test the effects of fungicides and microbes on pollination. </p> <p>As bloom progressed, bacterial and fungal abundance and diversity increased across all floral tissue types and management strategies. The magnitude by which microbial abundance and diversity were affected varied, with proximity to apiaries and orchard management having notable effects on bacteria and fungi, respectively.</p> <p>Experiments revealed that fungicides reduced nectar removal by honey bees; however, neither fungicide nor microbe treatments affected pollination, as measured through pollen tube initiation and growth. </p> <p><strong>Synthesis and applications</strong>: Our results reveal that microbiota associated with flowers of a pollinator-dependent crop are temporally dynamic and sensitive to management practices. However, pollination services in almonds may be resilient to both agrochemical disturbance and microbial augmentation of flowers, the latter of which may become more prominent as microbial solutions to disease management are embraced in agroecosystems.</p>
Data from: The potential of undersown species identity vs. diversity to manage disease in crops
<p>In the absence of chemical control with its negative side effects, fungal pathogens can cause large yield losses, requiring us to develop agroecosystems that are inherently disease resistant. Grassland biodiversity experiments often find plant species diversity to reduce pathogen pressure, but whether incorporating high biodiversity levels in agricultural fields have similar effects remains largely unknown.</p> <p>We tested if undersown plant species diversity could reduce barley disease, and whether the effect was mediated through above- or belowground mechanisms, by combining an agricultural field trial with a soil transplant experiment.</p> <p>As predicted, barley disease decreased in the presence of undersown plants. Undersown species richness had no effect, but their abundance led to early season disease reduction. Aboveground mechanisms underpinned this disease reduction. Barley yield slightly decreased with increasing undersown species richness, and undersown species varied in their impact on yield.</p> <p>We identified two undersown species, <em>Trifolium repens</em> and <em>T. hybridum</em>, that contributed most to disease reduction and had the potential to increase barley yield. Furthermore, our results indicate that aboveground mechanisms caused this. We show that agroecosystem functioning can be improved without trade-offs on yield by targeted selection of undersown species.</p>
Data from: The potential of undersown species identity vs. diversity to manage disease in crops
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Fair-weather friends: Priority determines disease outcomes in an agonistic multi-pathogen crop pathosystem
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Disease management during bloom affects the floral microbiome but not pollination in a mass-flowering crop
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PestReKNet-X: Integrating Explainable AI to enhance pest disease detection and combat crop senescence
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Data from: No evidence of foliar disease impact on crop root functional strategies and soil microbial communities: What does this mean for organic coffee?
<p><span>Global climate change is increasing pest and pathogen pressures on plant communities, deteriorating optimal plant functioning. In plant communities, root functional trait expression and microbial communities are important indicators of plant functioning belowground, and, when confronted with pathogens aboveground, can simultaneously reflect plant defence strategies. Yet, while research is continuing to emerge on the response of root functional traits and microbial processes to pathogens aboveground, little work has investigated these interactions in tree-crops, or the role organic amendments play in moderating these relationships. The main objective of this study is to disentangle the dynamic effects of pathogens and amendments on root functional traits (i.e., specific root length and area, root diameter, root length density, root nitrogen, and root carbon to nitrogen ratio) and root endophytic fungal communities. As a model, we use <em>Coffea arabica </em>(coffee) variety Caturra along a gradient of Coffee Leaf Rust – a foliar disease prominent in coffee systems – under contrasting but widespread amendment regimes in biodiverse agroforestry systems. We found that root trait expression varies along established conservation and collaboration gradients, where fungal endophyte community composition varies significantly as a function of root traits. Belowground resource acquisition strategies do not change with foliar disease incidence, suggesting they may be decoupled. Rather, amendment regimes </span>differentially shape root trait expression and microbial communities<span>, where coffee plants under organic amendments, regardless of foliar disease incidence, expressed greater acquisitive traits and enhanced collaboration with symbiotic fungi. </span>This is an important first step in disentangling the dynamic inter-relationships between plant traits, endophytes, and pathogens, generating new questions on the role of amendments in sustainable pathogen management in biodiverse agroecosystems.</p> <p> </p>
Genetic composition and diversity of Arabica coffee in the crop's center of origin and its impact on four major fungal diseases
<p><span>Conventional wisdom states that </span><span>genetic variation reduces disease levels in plant populations. Nevertheless, crop species have been subject to a gradual loss of genetic variation through selection for specific traits during breeding, thereby increasing their vulnerability to biotic stresses such as pathogens. We explored how genetic variation in Arabica coffee sites in southwestern Ethiopia was related to the incidence of four major fungal diseases. Sixty sites were selected along a gradient of management intensity, ranging from nearly wild to intensively managed coffee stands. We used genotyping-by-sequencing of pooled leaf samples (pool-GBS) derived from 16 individual coffee shrubs in each of the sixty sites to assess the variation in genetic composition (multivariate: reference allele frequency) and genetic diversity (univariate: mean expected heterozygosity) between sites. </span><span>We found that genetic composition had a clear spatial pattern and that genetic diversity was higher in less managed sites</span><span>. The incidence of the four fungal diseases was related to the genetic composition of the coffee stands, but in a specific way for each disease. In contrast, genetic diversity was only related to the within-site variation of coffee berry disease, but not to the mean incidence of any of the four diseases across sites. Given that fungal diseases are major challenges of Arabica coffee in its native range, our findings that genetic composition of coffee sites impacted the major fungal diseases may serve as baseline information to study the molecular basis of disease resistance in coffee. </span><span>Overall, our study illustrates the need to consider both host genetic composition and genetic diversity when investigating the genetic basis for variation in disease levels</span><span>. </span></p>
Decomposing cover crops modify root-associated microbiome composition and disease tolerance of cash crop seedlings
<p>The assembly of root-associated microbes during the seedling stage has strong impact on subsequent performance of crops. Major factors influencing this assembly are crop species identity and composition of potential root-colonizing microbes in the bulk soil. The latter can be modified by soil management, such as organic amendments. The incorporation of residues of cover crops before the start of the growing season of cash crops presents an interesting option for steering of root-associated seedling microbiomes as there is a wide range of cover crops species with different properties available for farmers.</p> <p>In a greenhouse study, we examined the effect of soil amendments with milled shoot and root materials of seven cover crop species (niger seed, phacelia, rapeseed, radish, vetch, black oat and buckwheat) on the soil nitrogen and biomass of seedlings of four cash crop species (asparagus, carrot, onion and sugar beet) and their root-associated bacteria and fungi. Field-grown cover crops material used for the study was collected at two time points (before and after winter) which had strong impact on plant elemental composition. Since the soil used for the study was a mixture of sandy arable soils with a history of soil-borne fungal diseases (Fusarium and Rhizoctonia), we also examined whether decomposing cover crop residues had an influence on the severity of damping-off diseases.</p> <p>Within the context of a strong selection of root-associated microbes by cash crop species, we found significant modifying effects by cover crop materials. High-quality residues (with low C/N ratio) caused profound shifts within root-associated Proteobacteria and increases in relative abundance of certain microbial groups such as Bacillaceae and Mortierellomycetes. These changes coincided with differences in establishment and survival of cash crop seedlings. This indicates that fine-tuning of cover crops amendments for different cash crops is required to realize enhanced functioning of root microbiomes.</p>
Advancing crop disease early warning in South Asia by complementing expert surveys with internet media scraping - supporting data and analysis
<p>Contains data from experiments and analysis that support the associated paper currently under review.</p> <p>Also contains scripts to:</p> <p>- Get the latest scraped media reports from the online dashboard (see doi: 10.5281/zenodo.8024493), and generate proxy surveys from them;</p> <p>- Simulate wheat rust spore production based on available survey datasets.</p> <p>- Analyse results of surveys, and models of spore production and spore dispersal, and reproduce the figures contained in the associated paper and its supplement.</p> <p>Requirements: Docker installed and 6GB of available storage space.</p> <p>Steps to use:</p> <p>1) Download and unzip the contained file.</p> <p>2) (Linux) Run setup.sh.</p> <p> (Windows/OSX/other) Look in setup.sh for instructions on where to unzip the files contained in supporting_data/ and build the Docker image and container.</p> <p>3) Start and enter the Docker container, then run /home/code/sources-media-reports/run_all.sh.</p>
Decomposing cover crops modify root-associated microbiome composition and disease tolerance of cash crop seedlings
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Genetic composition and diversity of Arabica coffee in the crop’s center of origin and its impact on four major fungal diseases
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Data from: No evidence of foliar disease impact on crop root functional strategies and soil microbial communities: What does this mean for organic coffee?
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WHEAT CROP DISEASE IMAGES CAPTURED BY DRONE
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Cotton Crop Diseases
<p>different cotton crop leaf diseases datasets, collected from southern Punjab, Pakistan</p>
Agricultural Crop Disease Image Dataset from Tanzania
<p>A comprehensive Crop Disease Image Dataset meticulously curated to aid in the study and advancement of agricultural research and plant pathology within Tanzania. This dataset encompasses a diverse array of classes, each representing distinct diseases and health states affecting a variety of crops commonly cultivated in Tanzanian agriculture. Classes include American Poppy, Bean Angular Leaf Spot, Bean Healthy, Bean Leaf Miner, Bean Light Yellow, Bean Rust, Bell Pepper Early Bright, Bell Pepper Fusarium Wilt, Bell Pepper Healthy, Black Nightshade Healthy, Black Nightshade Leaf Miner, Cabbage CP Deficiency, Cabbage Healthy, Chinese Botanical Leaf Spot, Chinese Healthy, Maize Faw, Maize Health, Maize Leaf Aphid, Maize Streak Virus, Okra Health, Okra Leaf Miner, Okra Powderly, Onions Healthy, Onions Powderly, Spinach Anthracnose, Spinach Health, Tomato Health, and Tomato Leaf Miner. Each class represents a distinct disease or health state commonly observed in crops grown throughout Tanzania, providing a valuable resource for researchers, agronomists, and machine learning practitioners alike. With a diverse range of classes meticulously labeled and organized, this dataset facilitates the development and evaluation of algorithms for disease detection, classification, and mitigation strategies specific to Tanzanian agricultural contexts.</p>
affy_med_2012_03-Characterization of new phytostimulatrices molecules for the improvement of plants products and crop resistance to diseases
GEO Series GSE43839. Sinorhizobium meliloti; Medicago sativa; Medicago truncatula. 27 samples. Type: Expression profiling by array.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.