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20 results for “crop detection”
Extra data to accompany code in GitHub burntfields_punjab, both used in Walker et. al. 2022, Detecting crop burning in India using satellite data
<p>Supplementary data files to accompany GitHub code 'burntfields_punjab' supporting Walker et. al. (2022) Detecting crop burning in India using satellite data [<a href="https://arxiv.org/abs/2209.10148">available here</a>] and Jack et. al. (2024) Money (not) to burn: Payments for ecosystem services to reduce crop residue burning).</p> <p>Includes custom Sentinel-2 cloud masks and data from Sentinel-2 Spectral Mixture Analysis to highlight Char (burning) based on general concept and methods from Daldegan et. al (2019). Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone. Remote Sensing of Environment 232, 111340. </p> <p>Note: Bands in weekly BASMA layers are: 0 = green vegetation, 1 = Non-productive vegetation and bare soil, 2 = Char (burned).</p> <p>further details are provided at: <a href="https://github.com/klwalker-sb/burntfields_punjab">https://github.com/klwalker-sb/burntfields_punjab</a> (archived at: <a href="https://doi.org/10.5281/zenodo.11225292" target="_blank" rel="noopener">DOI: 10.5281/zenodo.11225292</a>)</p>
Fig. 3. Evolutionary analysis conducted through a neighbor-joining phylogenetic tree between the cox1 in Detection of maize bushy stunt phytoplasma in leafoppers collected in native corn crops grown at high elevations in southeast Mexico
Fig. 3. Evolutionary analysis conducted through a neighbor-joining phylogenetic tree between the cox1 mini-barcode sequences obtained for the red speckled nymphs and Idiodonus wickhami (Hemiptera: Cicadellidae) (both marked with a circle) with reference sequences from GenBank. Bar 5 substitution in 100 positions.
Fig. 4. Evolutionary analysis conducted through a neighbor-joining phylogenetic tree between the 16S in Detection of maize bushy stunt phytoplasma in leafoppers collected in native corn crops grown at high elevations in southeast Mexico
Fig. 4. Evolutionary analysis conducted through a neighbor-joining phylogenetic tree between the 16S rRNA sequences amplified in this study from phytoplasma DNA, bar 1 substitution in 100 positions. Sequences in the grey square belong to the subgroup 16SrI-B. Sequences amplified from leafoppers (Hemiptera: Cicadellidae) Dalbulus elimatus marked with a circle and from Idiodonus wickhami marked with a square.
Fig. 7 in Detection of maize bushy stunt phytoplasma in leafoppers collected in native corn crops grown at high elevations in southeast Mexico
Fig. 7. Single nucleotide polymorphism of cpn60 UT sequences amplified from Dalbulus elimatus and Idiodonus wickhami compared with the 16SrI-B strains maize bushy stunt-Col (AB599712), maize bushy stunt-Pueb (KT444672), maize bushy stunt-Ver (KT444673), AVUT (AB599686), AY-27 (AB599688), and AY2192 (AB599687). (A) Similarities between the maize bushy stunt strains and the sequences obtained from the leafoppers. (B) Similarity between maize bushy stuntPueb and the phytoplasma detected associated with Idiodonus wickhami and similarity between maize bushy stunt-Col and maize bushy stunt-Ver with the phytoplasma associated with Dalbulus elimatus, based on SNP in cpn60 UT sequences.
Fig. 2 in Detection of maize bushy stunt phytoplasma in leafoppers collected in native corn crops grown at high elevations in southeast Mexico
Fig. 2. Idiodonus wickhami Ball. (Hemiptera: Cicadellidae). (A) Dorsal view, (B) ventral view, (C) vertex, pronotum and scutellum, (D) Male genitalia, (E-G) I. wickhami nymphs.
Fig. 6. Electrophoresis agarose gel showing RFLP pattern comparison between the F2 in Detection of maize bushy stunt phytoplasma in leafoppers collected in native corn crops grown at high elevations in southeast Mexico
Fig. 6. Electrophoresis agarose gel showing RFLP pattern comparison between the F2nR2 sequences amplified from Dalbulus elimatus and Idiodonus wickhami digested with AluI, BstUI, HaeIII, HinfI, and Tsp509I. Molecular weight (MW) marker, 1 kb plus.
Fig. 1 in Detection of maize bushy stunt phytoplasma in leafoppers collected in native corn crops grown at high elevations in southeast Mexico
Fig. 1. Six of the 7 leafopper genera (Hemiptera: Cicadellidae) detected in this study. Dorsal view of: (A) Dalbulus, (B) Macrosteles, (C) Amblysellus, (D) Graphocephala, (E) Erythridula, (F) Empoasca.
EOL computer vision pipelines: Object Detection for Image Cropping: Aves
<p>Produced by an detection model pretrained on MS COCO 2017. Automatically crops images of birds (Aves) to square dimensions centered around animal(s). </p> <p>388,166 rows </p>
EOL computer vision pipelines: Object Detection for Image Cropping: Multi-taxon
<p>Produced by EOL Multitaxon Object Detection Model. Automatically crops images of snakes & lizards (Squamata), beetles (Coleoptera), frogs (Anura), and carnivores (Carnivora) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/multitaxa-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <ul> <li>Anura = 42646 rows</li> <li>Coleoptera = 115276 rows</li> <li>Squamata = 132680 rows</li> <li>Carnivora = 31132 rows</li> </ul>
EOL computer vision pipelines: Object Detection for Image Cropping: Chiroptera
<p>Produced by EOL Chiroptera Object Detection Model. Automatically crops images of bats (Chiroptera) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/chiroptera-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <p> </p> <p>17,401 rows</p>
EOL computer vision pipelines: Object Detection for Image Cropping: Lepidoptera
<p>Produced by EOL Lepidoptera Object Detection Model. Automatically crops images of butterflies and moths (Lepidoptera) to square dimensions centered around animal(s). Model available in the <a href="https://www.kaggle.com/models/eolorg/lepidoptera-crops-thumbnails" target="_blank" rel="noopener">EOL Model Zoo on Kaggle</a>.</p> <p> </p> <p>608,163 rows</p> <p> </p>
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>
Fecal standing crop with real time correction using scat detection dogs to estimate population density
<p>Population density is fundamental information for assessing the conservation status of species and support management and conservation actions for in situ populations, but is unknown for many forest species due to their difficulty in detection. The Fecal Standing Crop (FSC) method using detection dogs is an alternative for cryptic or elusive species. An intrinsic difficulty of FSC is the ability to find fecal samples in the field and to estimate the probability of which feces detection is influenced by degradation due to climatic conditions. Our goal was to propose a concurrent FSC parameter estimation using a scat detection dog under different climatic conditions and apply those parameters in a wild deer population. Ten fecal samples of gray brocket deer (Subulo gouazoubira) were placed weekly in a transect (24 x 1200 m) in both dry and wet seasons (12 weeks each). A scat detection dog was then employed to find experimental fecal samples to determine the FSC parameters that were subsequently used with naturally occurring fecal samples (also dog-detected) to estimate population density. The oldest dog found samples were 21 (Dry) and seven (Wet) days after placement, resulting in dog efficiency of 23% (Dry) and 30% (Wet). Adjusting the model to account for efficiency and scat durability, we estimated similar, seasonal, densities of 4.54 individuals km-2 (SD = 2.21, Dry) and 5.52 indiv. km-2 (SD = 3.71, Wet).</p> <p><em>Synthesis and applications:</em> Our results demonstrate that our concurrent methodology corrected the effects of weather and habitat on FSC parameters thereby allowing for accurate population density estimation. Additionally, this method can provide reasonably precise density estimates with a logistically feasible sample size, as demonstrated by simulation. Following our recommendations, this method allows a reliable estimate of population density because it incorporates any influence of study area, dog ability, and climate in fecal sample detection, providing fundamental information for the conservation of many cryptic and elusive species.</p>
Fecal standing crop with real time correction using scat detection dogs to estimate population density
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PestReKNet-X: Integrating Explainable AI to enhance pest disease detection and combat crop senescence
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Data from: Crop-associated virus infection in a native perennial grass: reduction in plant fitness and dynamic patterns of virus detection
To understand the eco-evolutionary significance of plant viruses in nature, we must (i) quantify the effects of infection on plant fitness and (ii) recognize that native plants are increasingly exposed to crop-associated viruses. Studies of perennials are particularly needed: most of our knowledge of plant-virus interactions is from annuals, yet long-lived species dominate landscapes. Here we used aster models for life-history analysis and longitudinal measures of plant virus status to evaluate multi-year consequences of crop virus infection in a native perennial. We used Barley yellow dwarf virus acquired from wheat to inoculate seedlings of Panicum virgatum L. (switchgrass), a North American prairie grass. We grew inoculated and mock-inoculated individuals of two ecotypes for 3 years in the field. We measured plant size, infection status and fitness components. Aster modelling provided integrated multi-year measures of fitness. Crop virus inoculation reduced multi-year native plant fitness by 30% over 2 years despite generally asymptomatic infection and evidence of resistance. This reduction was greater than predicted from individual fitness components or most size measures. Ecotypes differed in response, with the lowland ecotype experiencing higher apparent recovery from infection. Virus treatment in the upland ecotype delayed flowering phenology and reduced seed filling. Synthesis. Our use of field experimentation, surveys of plant infection status and aster modelling demonstrates a rigorous and broadly applicable approach for quantifying the effects of viruses and other microbes on multi-year plant fitness. We found that a crop virus had negative multi-year effects on native plant fitness even after infection was no longer detected. Viruses may have substantial effects on native vegetation with domestication of landscapes and agricultural expansion.
Data from: Crop-associated virus infection in a native perennial grass: reduction in plant fitness and dynamic patterns of virus detection
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Data from: High fidelity detection of crop biomass QTL from low-cost imaging in the field
Field-based, rapid, and non-destructive techniques for assessing plant productivity are needed to accelerate the discovery of genotype-to-phenotype relationships in next-generation biomass grass crops. The use of hemispherical imaging and light attenuation modeling was evaluated against destructive harvest measures with respect to their ability to accurately capture phenotypic and genotypic relationships in a field-grown grass crop. Plant area index (PAI) estimated from below-canopy hemispherical images, as well as a suite of thirteen traits assessed by manual destructive harvests, were measured in a Setaria recombinant inbred line mapping population segregating for aboveground productivity and architecture. A significant correlation was observed between PAI and biomass production across the population at maturity (r2 = 0.60), as well as for select diverse genotypes sampled repeatedly over the growing season (r2 = 0.79). Twenty-seven quantitative trait loci (QTL) were detected for manually collected traits associated with biomass production. Of these, twenty-one were found in four clusters of co-localized QTL. Analysis of image-based estimates of PAI successfully identified all four QTL hotspots for biomass production. QTL for PAI had greater overlap with those detected for traits associated with biomass production than with those for plant architecture and biomass partitioning. Hemispherical imaging is an affordable and scalable method, which demonstrates how high-throughput phenotyping can identify QTL related to biomass production of field trials in place of destructive harvests that are labor, time, and material intensive.
Data from: High fidelity detection of crop biomass QTL from low-cost imaging in the field
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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.