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21 results for “agricultural weeds”
Swiss public's acceptance and sustainability perceptions of food produced with chemical, digital and mechanical weed control measures and the influence of information source on technology perception in agriculture
<p><span>This data was obtained from an online survey conducted with the Swiss public from the two biggest language regions (German and French) in Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age, gender and language region. The final sample contained </span><span>542 respondents. </span><span>In the first part of the survey, respondents provided basic sociodemographic information. In the second part, their sustainability perceptions regarding four different weed management practices (full-surface spraying, hoeing machine, spot spraying and precise spraying) were investigated. Respondents were then assigned to one of five information source groups, in which information on a hoeing and a milking robot was presented, using 5 different information sources (male/female farmer, male/female scientist, no source). Technology perception was assessed using several questions and aspects. Finally, respondents answered several questions assessing their attitudes towards the perception of farmers, food technology neophobia, chemophobia and the importance of naturalness. The survey can be used and adapted to different contents, aiming to investigate public perception of smart farming technologies and the influence of information sources on technology perception. </span></p>
RafanoSet: Dataset of raw, manual and automatically annotated Raphanus Raphanistrum weed images for object detection and segmentation in Heterogenous Agriculture Environment
<p>This dataset is a collection of raw and annotated Multispectral (MS) images acquired in a heterogenous agricultural environment with MicaSense RedEdge-M camera. The spectra particularly Green, Blue, Red, Red Edge and Near Infrared (NIR) were acquired at sub-metre level.. <br><br>The MS images were labelled manually using VIA and automatically using Grounding DINO in combination with Segment Anything Model. The segmentation masks obtained using these two annotation techniqes over as well as the source code to perform necessary image processing operations are provided in the repository. The images are focussed over Horseradish (Raphanus Raphanistrum) infestations in Triticum Aestivum (wheat) crops.</p> <p>The nomenclature of sequecncing and naming images and annotations has been in this format: IMG_<scene number>_<spectral channel number><br><strong>_1</strong>: Blue<br><strong>_2</strong>: Green<br><strong>_3</strong>: Red<br><strong>_4</strong>: Near Infrared<br><strong>_5</strong>: RedEdge<br><br>Example: An image name <strong>IMG_0200_3 </strong>represents the scene number<strong> 200</strong> in <strong>Red channel</strong></p> <p>This dataset 'RafanoSet'is categorized in 6 directories namely 'Raw Images', 'Manual Annotations', 'Automated Annotations', 'Binary Masks - Manual', 'Binary Masks - Automated' and 'Codes'. The sub-directory 'Raw Images' consists of manually acquired 85 images in .PNG format. over 17 different scenes. The sub-directory 'Manual Annotations' consists of annotation file 'region_data' in COCO segmentation format. The sub-directory 'Automated Annotations' consists of 80 automatically annotated images in .JPG format and 80 .XML files in Pascal VOC annotation format.</p> <p>The scientific framework of image acquisition and annotations are explained in the Data in Brief paper which is the course of peer review. This is just a prerequisite to the data article. <br><br>Field experimentation roles:</p> <p>The image acquisition was performed by Mariano Crimaldi, a researcher, on behalf of Department of Agriculture and the hosting institution University of Naples Federico II, Italy.</p> <p>Shubham Rana has been the curator and analyst for the data under the supervision of his PhD supervisor Prof. Salvatore Gerbino. They are affiliated with Department of Engineering, University of Campania 'Luigi Vanvitelli'. </p> <p>Domenico Barretta, Department of Engineering has been associated in consulting and brainstorming role particularly with data validation, annotation management and litmus testing of the datasets.</p>
Data for: Functional redundancy of weed seed predation is reduced by intensified agriculture
<p>Intensive agriculture, a driver of biodiversity loss, can diminish ecosystem functions and their stability. Biodiversity can increase functional redundancy and is expected to stabilize ecosystem functions. Few studies however have explored how agricultural intensity affects functional redundancy and its link with ecosystem function stability. Here, within a continent-wide study, we assess how the functional redundancy of seed predation is affected by agricultural intensity and landscape simplification. By combining carabid abundances with molecular gut content data, functional redundancy of seed predation was quantified for 65 weed genera across 60 fields in four European countries. Across weed genera, functional redundancy was reduced with high field management intensity and simplified crop rotations. Moreover, functional redundancy increased the spatial stability of weed seed predation within fields. We found that ecosystem functions are vulnerable to disturbance in intensively managed agroecosystems, providing empirical evidence of the importance of biodiversity for stable ecosystem functions across space.</p>
Figure 4 in Agricultural Research Service Weed Science Research: Past, Present, and Future
Figure 4. Agricultural Research Service researchers have focused on understanding how climate change influences weeds/invasive plants and their impacts and management. Image shows a study of how precipitation change influences cheatgrass (Bromus tectorum) invasion in rangelands of northeast Wyoming, USA. (Credit: Anna Kuhne)
Figure 5. A in Agricultural Research Service Weed Science Research: Past, Present, and Future
Figure 5. A conceptual framework providing context for addressing challenges for managing weeds in agroecosystems and natural areas.
Figure 3. Sorgoleone has a in Agricultural Research Service Weed Science Research: Past, Present, and Future
Figure 3. Sorgoleone has a potential as a plant-incorporated protectant herbicide. The highly bioactive benzoquinone allelochemical sorgoleone is produced exclusively within root hair cells of members of the genus Sorghum. Genes of the entire sorgoleone pathway are being used to transform crops to produce sorgoloeone (Pan et al. 2021).
Figure 2 in Agricultural Research Service Weed Science Research: Past, Present, and Future
Figure 2. (A) Depiction of a bipartite synthetic Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) cassette (red) consisting of a Cas9 endonuclease and a guide ribonucleic acid RNA (gRNA) that is flanked by homology arms (HA) (black). (B) Following expression of the CRISPR cassette, gRNA binds to Cas9 and directs the complex to a unique sequence-specific site for DNA cleavage and homology-directed repair (HDR). Following HDR, the CRISPR cassette is copied into both genomic regions. (C) Standard Mendelian inheritance results in 50% of progeny inheriting a modified gene. In contrast, a gene drive would bias inheritance, theoretically resulting in all progenies (~99%) inheriting the modified gene, thereby "driving" the modified gene into an invasive weed population.
Figure 1 in Agricultural Research Service Weed Science Research: Past, Present, and Future
Figure 1. (A) BenchBot autonomous high-throughput imaging system, (B) example imagery from BenchBot after automated segmentation of weeds from background objects, (C) testing of the handheld version of the Weeds3D system at the Beltsville Agricultural Research Center, and (D) example 3D reconstruction of plant biomass from the Weeds3D systems.
CornWeed Dataset: A dataset for training maize and weed object detectors for agricultural machines
<p>There are many datasets available for training object detectors in non agricultural domains such as Autonomous Driving but these datasets fail to generalize well enough to an agricultural use-case. This dataset contains 3574 hand labelled images with bounding boxes provided in both YOLO and COCO dataset format. Additionally there are 4981 unlabelled images for testing purposes. All the images are hand labelled with bounding boxes with several labellers and reviewed. The dataset contains two class IDs namely maize and weeds. The dataset also contains a crop-row instance but has not been used in the accompanying paper but could be interesting for other future work. The images have been recorded in two resolutions i.e. 720 x 1280 and 480 x 640 covering two crop rows and single crop row respectively. </p>
Limits to the evolution of herbicide escape and tolerance in the agricultural weed Amaranthus palmeri
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Data for: Functional redundancy of weed seed predation is reduced by intensified agriculture
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Responses of agricultural weed community in a corn-soybean intercrop
This dataset was created as a part of an experiment which used a soybean-corn intercrop system for examining how the community structure of agricultural weeds changes with fertilization and with the identity of the crop species. The composition of the weed flora in intercrops was compared with the weed flora of the respective sole crops, with and without fertilization. The experiment was conducted at Kellogg Biological Station (KBS) in southwestern Michigan, USA, in 1993.
Data from : Both long-term grasslands and crop diversity are needed to limit pest and weed infestations in agricultural landscapes
<p>Data used for analysis in Both long-term grasslands and crop diversity are needed to limit pest and weed infestations in agricultural landscapes </p>
Data from: Malaysian weedy rice shows its true stripes: wild Oryza and elite rice cultivars shape agricultural weed evolution in Southeast Asia
Weedy rice is a close relative of domesticated rice (Oryza sativa) that competes aggressively with the crop and limits rice productivity worldwide. Most genetic studies of weedy rice have focused on populations in regions where no reproductively compatible wild Oryza species occur (North America, Europe, northern Asia). Here we examined the population genetics of weedy rice in Malaysia, where wild rice (O. rufipogon) can be found growing in close proximity to cultivated and weedy rice. Using 375 accessions and a combined analysis of 24 neutral SSR loci and two rice domestication genes (sh4, controlling seed shattering, and Bh4, controlling hull color), we addressed the following questions: 1) What is the relationship of Malaysian weedy rice to domesticated and wild rice, and to weedy rice strains in the US? 2) To what extent does the presence of O. rufipogon influence the genetic and phenotypic diversity of Malaysian weeds? 3) What do the distributions of sh4 and Bh4 alleles and associated phenotypes reveal about the origin and contemporary evolution of Malaysian weedy rice? Our results reveal: independent evolutionary origins for Malaysian weeds and US strains, despite their very close phenotypic resemblance; wild-to-weed gene flow in Malaysian weed populations, including apparent adaptive introgression of seed-shattering alleles; and a prominent role for modern Malaysian cultivars in the origin and recent proliferation of Malaysian weeds. These findings suggest that the genetic complexity and adaptability of weedy crop relatives can be profoundly influenced by proximity to reproductively compatible wild and domesticated populations.
Data from: Malaysian weedy rice shows its true stripes: wild Oryza and elite rice cultivars shape agricultural weed evolution in Southeast Asia
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Data from: Honey bee dietary neonicotinoid exposure is associated with pollen collection from agricultural weeds
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Data from: Population genetics and origin of the native North American agricultural weed waterhemp (Amaranthus tuberculatus)
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Data from: Agricultural adaptation in the native North American weed waterhemp, Amaranthus tuberculatus (Amaranthaceae)
<p>There is increasing interest in documenting adaptation of weedy plant species to agricultural ecosystems, beyond the evolution of herbicide resistance. Waterhemp (<i>Amaranthus tuberculatus</i>) is a native plant of the Midwestern U.S. that began infesting agricultural fields in the 20<sup>th</sup> century within the central portion of its range. We hypothesized that the vegetative growth and reproductive traits of waterhemp from this heavily infested central region provide differential fitness benefits in agricultural environments. We collected seeds from across the species' native range, representing regions with varying degrees of waterhemp infestation, and planted them together in common garden soybean plots. A 2010 common garden experiment was conducted within the range of agriculturally weedy waterhemp (in Missouri), and a 2011 common garden experiment was conducted outside of this range (in Ohio). Days to flowering and flowering plant height, mature plant size data (height, number of branches, and length of the longest branch), and above-ground biomass were measured to estimate relative fitness. In both common garden locations, plants from regions where waterhemp occurs as an agricultural weed — including those from the heavily infested Mississippi Valley region (Iowa, Illinois, and Missouri) and the less severely infested Plains region (Nebraska, Kansas, and Oklahoma) — had higher relative performance in almost all fitness-related measures than plants from the Northeast region (Ohio, Michigan, and Ontario), which had little to no agriculturally weedy waterhemp at the time of our study. Further analysis revealed that fewer days to flowering in the Northeast populations can be largely accounted for by latitude of origin, suggesting a strong genetic influence on this reproductive trait. These findings suggest intraspecific variation in agricultural adaptation in a native U.S. weed, and support the use of agricultural weeds to study adaptation.</p>
Data from: A resurrection experiment finds evidence of both reduced genetic diversity and potential adaptive evolution in the agricultural weed Ipomoea purpurea
Despite the negative economic and ecological impact of weeds, relatively little is known about the evolutionary mechanisms that influence their persistence in agricultural fields. Here, we use a resurrection approach to examine the potential for genotypic and phenotypic evolution in Ipomoea purpurea, an agricultural weed that is resistant to glyphosate, the most widely used herbicide in current-day agriculture. We found striking reductions in allelic diversity between cohorts sampled nine years apart (2003 vs. 2012), suggesting that populations of this species sampled from agricultural fields have experienced genetic bottleneck events that have led to lower neutral genetic diversity. Heterozygosity excess tests indicate that these bottlenecks may have occurred prior to 2003. A greenhouse assay of individuals sampled from the field as seed found that populations of this species, on average, exhibited modest increases in herbicide resistance over time. However, populations differed significantly between sampling years for resistance: some populations maintained high resistance between the sampling years whereas others exhibited increased or decreased resistance. Our results show that populations of this noxious weed, capable of adapting to strong selection imparted by herbicide application, may lose genetic variation as a result of this or other environmental factors. We probably uncovered only modest increases in resistance on average between sampling cohorts due to a strong and previously identified fitness cost of resistance in this species, along with the potential that nonresistant migrants germinate from the seed bank.
Data from: A resurrection experiment finds evidence of both reduced genetic diversity and potential adaptive evolution in the agricultural weed Ipomoea purpurea
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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.