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52 results for “plant detection”
Ground truth and raw hyperspectral files of olive trees for plant stress detection
<p>This dataset contains raw hyperspectral images from Cubert S-185 collected on 13 May 2021 from an olive field in Halkidiki, Northern Greece. Included is also a matrix containing the id of each recorded olive tree (the samples) that also appears in the hyperspectral images. QGIS (ver.3.28.0) software plugin 'zonal statistics multiband' was used to compute zonal statistics for each of the 138 spectral bands available for each sample. Accompanying each sample is also the ground truthing data recorded, which addresses the present stress of 3 stressors (<i>Verticillium dahliae, Pleospora herbarum </i>and 'other stressors').</p>
VirHunter: a deep learning-based method for detection of novel RNA viruses in plant sequencing data
<p>This storage contains 2 archives: toy datasets to test the training of the VirHunter and weights of the fully trained VirHunter models for 3 host species (peach, grapevine, sugar beet) and for fragment sizes 500 and 1000. .</p> <p>The toy dataset consists of 3 archived files: 'viruses.fasta', 'host.fasta', 'bacteria.fasta'.</p> <p>'viruses.fasta' contains 10000 randomly selected plant viruses from the virus dataset described in the paper.</p> <p>'host.fasta' consists of peach chromosome 2.</p> <p>'bacteria.fasta' consists of 10 bacterial genomes selected randomly: GCF_000284415, GCF_000590555, GCF_001548055, GCF_002795265, GCF_003330825, GCF_003957805, GCF_005845345, GCF_009176625, GCF_010748935, GCF_014681765</p> <p> </p>
Web sourced dataset for plant disease detection
<p>The web-sourced dataset consists of plant leaf images collected from online platforms, primarily through sources like Google Images, to capture a wide range of real-world scenarios and environmental conditions. Unlike controlled laboratory datasets, these images feature diverse backgrounds, lighting variations, and different stages of plant diseases, representing how diseases appear in natural agricultural settings. The dataset includes multiple plant species and disease types, augmenting existing datasets by adding greater variability. This diversity aims to improve model robustness and generalization, enabling more accurate disease detection across varying agricultural environments.</p>
An Analysis of Plant Disease and Their Detection
<p><span>One of the biggest revolutions of modern history is the invention of agriculture for a healthier lifestyle. It significantly changed the human culture and played an important role in the development of the population and biological improvements in food production and domestication. The frequency of pests on food crops increased because environmental circumstances were changing, and diseases on crops increased rapidly. These diseases inflict catastrophic social, economic, and ecological casualties, and this extraordinary challenge is a concern for the correct and prompt detection of diseases. In this contest, technology has left its mark on the potential of farmers and is still to be exploited. As input for making the right decision, farmers need timely and credible sources of knowledge. Study into Agriculture have to be planned by improving the disease diagnostics method with the use of newer technology to enhance efficiency and quantity for agricultural production and its allied operation. In various applications of the agricultural industry, computer methodologies have been used for automation. Timely farming decisions and disease management are taken using image analysis and machinery of learning techniques in planning and creating a method for the diagnosis of diseases.</span></p>
Data from: A tale of two studies: detection and attribution of the impacts of invasive plants in observational surveys
1.Short-term experiments cannot characterize how long-lived, invasive shrubs influence ecological properties that can be slow to change, including native diversity and soil fertility. Observational studies are thus necessary, but often suffer from methodological issues. 2.To highlight ways of improving the design and interpretation of observational studies that assess the impacts of invasive plants, we compare two studies of nutrient cycling and earthworms along two separate gradients of invasive shrub abundance. By considering the divergent sampling strategies and statistical analyses of these two studies, and interpreting their contradictory results in the context of other studies, we also aim to better describe the impacts of the focal invader, Rhamnus cathartica. 3.In a new study of a single site in Minnesota, we observed positive correlations between buckthorn abundance and soil pH, soil nutrient pools, nutrient fluxes through leaf litterfall, earthworm abundance, and root biomass. Multiple regression models showed these relationships persisted after accounting for variability in soil texture and tree species composition. For a separate, more expansive study in Illinois, other authors reported little to no correlation between buckthorn abundance and 10 soil properties, including earthworm abundance, pH, and nutrient concentrations. However, like many other studies, their regression models only assessed predictors related to invader abundance. R2 values for models of ecosystem properties ranged from 0-0.79 (adjusted-R2) for our study in Minnesota and from <0.05-0.16 (unadjusted) for the prior study in Illinois. 4.Differences in sampling error and use of predictor variables between the two studies likely explain the contrasting results. 5.Synthesis and applications. To reduce the uncertainty of conclusions from observational studies of invasive plants, future studies must ensure that heterogeneity of soils and vegetation is adequately accounted for in the sampling strategy and statistical analyses (e.g., analysis of covariance, multiple regression). Particular attention should be given to ecosystem properties with variability that likely predates the invader (e.g., geophysical features and tree community composition). In our study, effects of buckthorn on ecosystem properties were not only robust to the inclusion of potentially confounding predictors, but also consistent with expectations based on ecological stoichiometry and mass balance of element flow.
Public resources about PHYVV and TMV detection in Jalapeño pepper plants using CNNs
<p>This repository contains a dataset of Jalapeño pepper leaves which where divided into three classes labeles as "healthy", "PHYVV-infected" and "TMV-infected. In addition, the folder labeled as model contains all the information of the artificial intelligence model that was utilized to classify between the aforementioned classes.</p>
Data from: A tale of two studies: detection and attribution of the impacts of invasive plants in observational surveys
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Imperfect detection in plant populations can cause misestimates of demographic rates and missed population trends: The case for Astragalus microcymbus Barneby
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Minimal assay detects population-level senescence in the aquatic plant Lemna minor
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Data: Detecting preservation and reintroduction sites for endangered plant species using a two-step modelling and field approach
<p><span>To withstand the surge of species loss worldwide, (re)introduction of endangered plant species has become an increasingly common technique in conservation biology. Successful (re)introduction plans, however, require identifying sites that provide the optimal ecological conditions for the target species to thrive. In this study, we propose a two-step approach to identify appropriate (re)introduction sites. The first step involves modelling the niche and distribution of the species with bioclimatic and topographical predictors, both at continental and at national scales. The second step consists of refining these bioclimatic predictions by analysing stationary ecological parameters, such as soil conditions, and relating them to population-level fitness values. We demonstrate this methodology using Swiss populations of the lady's slipper orchid (<em>Cypripedium calceolus</em> L., Orchidaceae), for which conservation plans have existed for years but have generally been unfruitful. Our workflow identified sites for future (re)introductions based on the species requirements for mid-to-sunny light conditions and specific soil physico-chemical properties, such as basic to neutral pH and low soil organic matter content. Our findings show that by combining wide-scale bioclimatic modelling with fine scale field measurements it is possible to carefully identify the ecological requirements of a target species for successful (re)introductions.</span></p>
Neighbor-detection causes shifts in allocation across multiple organs to prepare plants for light competition
<p>To maximize their fitness, plants have to adjust their allocation strategy according to their abiotic and biotic environments. Plants can use the ratio of red to far-red light (R:FR) to sense neighbors, allowing them to modify their growth in response to aboveground competition.</p> <p>In this study, we used supplemental FR light to artificially lower the R:FR of the lower leaves of common sunflowers (Helianthus annuus) to examine how plants change their growth in response to the threat of neighbors. We combined this treatment with a nitrogen fertilization treatment to investigate how responses to neighbor-detection interact with nitrogen limitation.</p> <p>Plants grown in low R:FR increased in height at the expense of root growth, resulting in nitrogen limitation that restricted leaf growth. However, we found that plants reduced their nitrogen investment into leaves in low R:FR. By weakening the nitrogen sink strength of these lower leaves before they experienced low photosynthetically active radiation, plants were able to preemptively allocate nitrogen to leaves higher in the canopy.</p> <p>Plants responded to the perception of neighbors by simultaneously diverting resources from root growth to stem elongation and from leaves threatened by neighbors to leaves that would pose a threat to neighbors. This whole-plant response to neighbor-detection enables plants to change their allocation in a way that simultaneously manages their limited nitrogen and prepares them for future light competition.</p>
Additional files for Horvath et al., 2024. Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning.
<p>Additional data for Horvath et al., 2024 (source code freeze, models, data, supplementary figures, tables and files(.</p>
Variation in plant leaf traits affects transmission and detectability of herbivore vibrational cues
<p>Many insects use plant-borne vibrations to obtain important information about their environment, such as where to find a mate or a prey, or when to avoid a predator. Plant species can differ in the way they vibrate, possibly affecting the reliability of information, and ultimately the decisions that are made by animals based on this information. We examined whether the production, transmission and possible perception of plant-borne vibrational cues is affected by variation in leaf traits. We recorded vibrations of 69 <i>Spodoptera exigua</i> caterpillars foraging on four plant species that differed widely in their leaf-traits (cabbage, beetroot, sunflower and corn). We carried out a transmission and an airborne noise absorption experiment to assess whether leaf traits influence amplitude and frequency characteristics, and background noise levels of vibrational-chewing cues. Our results reveal that species-specific leaf traits can influence transmission and potentially perception of herbivore-induced chewing vibrations. Experimentally-induced vibrations attenuated stronger on plants with thicker leaves. Amplitude and frequency characteristics of chewing vibrations measured near a chewing caterpillar were, however, not affected by leaf traits. Furthermore, we found a significant effect of leaf area, water content and leaf thickness - important plant traits against herbivory, on the vibrations induced by airborne noise. On larger leaves higher amplitude vibrations were induced, whereas on thicker leaves containing more water airborne noise induced higher peak frequencies. Our findings indicate that variation in leaf traits can be important for the transmission and possibly detection of vibrational cues.</p>
Dataset for: Designing a surveillance program for early detection of alien plants and insects in Norway
<p><span>Naturalized species of alien plants and animals comprise < 3% of biodiversity recorded in Norway but have had major impacts on natural ecosystems through displacement of native species. Encroachment of alien species has been especially problematic for coastal sites close to transport facilities and urban areas with high-density housing. The goal of our field project was to design and test a surveillance program for early detection of alien species of vascular plants and terrestrial insects at the first phase of establishment in natural areas. In our 3-year project (2018–2020), we sampled 60 study plots in three counties in the Oslofjord region of southern Norway. Study plots (6.25 ha) were selected by two criteria: manual selection based on expert opinion (27 plots) or by random selection based on weights from a hotspot analysis of occurrence of alien species (33 plots). Vascular plants were surveyed by two experienced botanists who found a total of 239 alien species of vascular plants in 95 rounds of surveys. Insects and other invertebrates were captured with a single Malaise trap per site, with 3-4 rounds of repeated sampling. We used DNA-metabarcoding to identify invertebrates based on DNA extractions from crushed insects or from the preservative media. Over 3,500 invertebrate taxa were detected in 255 rounds of sampling. We recorded 20 alien species of known risk and 115 species that were new to Norway, including several 'doorknocker' species identified by previous risk assessments. We modeled the probabilities of occupancy (</span><span>y</span><span>) and detection (p) with occupancy models with repeated visits by multiple observers (vascular plants) or multiple rounds of sampling (insects). The two probabilities covaried with risk category for alien organisms and both were low for species categorized as no known or low risk (range = 0.052 to 0.326) but were higher for species categorized as severe risk (range = 0.318 to 0.651). Selecting sites at random or manually did not improve the probability of finding novel alien species, but occupancy had a weak positive relationship with housing density for some categories of alien plants and insects. We used our empirical estimates to test alternative sampling designs that would minimize the combined variance of occupancy and detection (A-optimality criterion). Sampling designs with 8–10 visits per site were best for surveillance of new alien species if the probabilities of occupancy and detection were both low, and provided low conditional probabilities of site occupancy (psi-hat(cond) </span><span>£</span><span> 0.032) and a high probabilities of cumulative detection (p-hat(star) </span><span>³</span><span> 0.943). Our field results demonstrate that early detection is feasible as a key component of a national surveillance program based on early detection and rapid response (EDRR). </span></p>
Does pre-sorting by colour using visible and high-energy violet light improve the detection of plant species in honey bee pollen baskets?
<div class="article-section__content en main"> Premise <p>Pollen collected by honey bees from different plant species often differs in color, and this has been used as a basis for plant identification. The objective of this study was to develop a new, low-cost protocol to sort pollen pellets by color using high-energy violet light and visible light to determine whether pollen pellet color is associated with variations in plant species identity.</p> Methods and Results <p>We identified 35 distinct colors and found that 52% of pollen subsamples (<em>n</em> = 200) were dominated by a single taxon. Among these near-pure pellets, only one color consistently represented a single pollen taxon (Asteraceae: Cichorioideae). Across the spectrum of colors spanning yellows, oranges, and browns, similarly colored pollen pellets contained pollen from multiple plant families ranging from two to 13 families per color.</p> Conclusions <p>Sorting pollen pellets illuminated under high-energy violet light lit from four directions within a custom-made light box aided in distinguishing pellet composition, especially in pellets within the same color.</p> </div>
Data from: Plant-derived environmental DNA complements diversity estimates from traditional arthropod monitoring methods but outperforms them detecting plant-arthropod interactions
<p>Our limited knowledge about the ecological drivers of global arthropod decline highlights the urgent need for more effective biodiversity monitoring approaches. Monitoring of arthropods is commonly performed using passive trapping devices, which reliably recover diverse communities, but provide little ecological information on the sampled taxa. Especially the manifold interactions of arthropods with plants are barely understood. A promising strategy to overcome this shortfall is environmental DNA (eDNA) metabarcoding from plant material on which arthropods have left DNA traces through direct or indirect interactions. However, the accuracy of this approach has not been sufficiently tested. In four experiments, we exhaustively test the comparative performance of plant-derived eDNA from surface washes of plants and homogenized plant material against traditional monitoring approaches. We show that the recovered communities of plant-derived eDNA and traditional approaches only partly overlap, with eDNA recovering various additional taxa. This suggests eDNA as a useful complementary tool to traditional monitoring. Despite the differences in recovered taxa, estimates of community α- and β-diversity between both approaches are well correlated, highlighting the utility of eDNA as a broad scale tool for community monitoring. Last, eDNA outperforms traditional approaches in the recovery of plant-specific arthropod communities. Unlike traditional monitoring, eDNA revealed fine-scaled community differentiation between individual plants and even within plant compartments. Especially specialized herbivores are better recovered with eDNA. Our results highlight the value of plant derived eDNA analysis for large-scale biodiversity assessments that include information about community level interactions.</p>
Data for: Detection of oomycete pathogens in UK peat-free growing media and implications for plant health
<p>This dataset on Zenodo accompanies the manuscript Frederickson-Matika <em>et al.</em> (2024), Detection of oomycete pathogens in UK peat-free growing media and implications for plant health.</p> <p>There are two files:</p> <ul> <li>metadata.tsv - plain text table as tab-separated variables</li> <li>raw_data.tar.gz - compressed archive of 43 paired raw FASTQ files</li> </ul> <p>This represents a subset of two complete Illumina MiSeq plates (in two dated folderes) run at the James Hutton Institute containing other environmental samples using the same protocol. Only the synthetic controls and peat-free samples are provided here.<br><br>To repeat the analysis described in the paper, first install THAPBI PICT. See <a href="https://github.com/peterjc/thapbi-pict/">https://github.com/peterjc/thapbi-pict/ </a>for instructions. At the time of the paper, v1.0.14 was the current release.</p> <p>Next, decompress the raw data into a folder of paired gzipped FASTQ files. There is no need to decompress those:</p> <pre><code> $ tar -zxvf raw_data.tar.gz<br> $ ls -1 plate_20220505/ plate_20230608/</code></pre> <p>If you wish, verify the checksums to confirm the data integrity:</p> <pre><code> $ cd plate_20220505/ $ md5sum -c MD5SUM.txt<br> $ cd ../plate_20230608/ $ md5sum -c MD5SUM.txt<br> $ cd ..</code></pre> <p>Setup output directories:</p> <pre><code><code> $ mkdir -p intermediate/ summary/</code></code></pre> <pre>Run the THAPBI PICT pipeline:</pre> <pre><code> $ thapbi_pict pipeline -m 1s3g \<br> -i plate_*/ -o summary/peat-free \<br> -y plate_*/GBL*.fastq.gz \<br> -n plate_*/GBL*.fastq.gz \<br> -s intermediate/ \<br> -t metadata.tsv -u \<br> -x 9 -c 1,2,3,4,5,6,7,8</code><br><br></pre> <p>The options here are as follows:</p> <ul> <li>-i - two input directories of paired raw FASTQ files.</li> <li>-n - negative controls used to increase the absolute abundance threshold</li> <li>-y - synthetic controls used to increase the fractional abundance threshold</li> <li>-s - optional location to store intermediate files</li> <li>-o - output stem for reports</li> <li>-t - filename for tab-separated-variable metadata</li> <li>-u - show unsequenced samples defined in the metadata</li> <li>-x - which metadata column contains Illumina FASTQ filename stems</li> <li>-c - which metadata columns to include in the report.</li> </ul> <p>This assumes the following key default settings:</p> <ul> <li>-a 100 (default absolite abundance threshold)</li> <li>-f 0.001 (default fractional abundance threshold)</li> <li>-d -(default provided ITS1 database).</li> </ul> <p>With these settings, only synthetic sequences were found in the controls, and therefore the thresholds were not automatically increased any further.</p> <p>Opening the output file summary/peat-free.ITS1.samples.1s3g.xlsx in Excel or similar should show you a table resembling Table 1 in the paper, but one row per sequencing sample, and additional columns with per-sample per-species read counts etc.</p>
Dataset for: Designing a surveillance program for early detection of alien plants and insects in Norway
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Data from: Plant-derived environmental DNA complements diversity estimates from traditional arthropod monitoring methods but outperforms them detecting plant-arthropod interactions
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Data from: Importance of accounting for imperfect detection of plants in the estimation of population growth rates
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.