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422 results for “Weed”
Fig. 2 in Brazilian collections and laboratory biology of the thrips Pseudophilothrips ichini (Thysanoptera: Phlaeothripidae): a potential biological control agent of the invasive weed Brazilian peppertree (Sapindales: Anacardiaceae)
Fig. 2. Life history stages of the thrips Pseudophilothrips ichini reared on leaves of Schinus terebinthifolia in quarantine at the United States Department of Agriculture, Agricultural Research Service, Invasive Plant Research Laboratory (horizontal bars = 0.5 mm).
Fig. 1 in Brazilian collections and laboratory biology of the thrips Pseudophilothrips ichini (Thysanoptera: Phlaeothripidae): a potential biological control agent of the invasive weed Brazilian peppertree (Sapindales: Anacardiaceae)
Fig. 1. Map showing the distribution of the host Brazilian peppertree, Schinus terebinthifolia, in its native range (black dots) and the thrips Pseudophilothrips ichini (red dots). Thrips introduced to quarantine for life history studies were collected from a population near Ouro Preto, Minas Gerais, Brazil.
Fig. 1 in Utilization of an introduced weed biological control agent, Megamelus scutellaris (Hemiptera: Delphacidae), by a native parasitoid
Fig. 1. Kalopolynema ema (Hymenoptera: Mymaridae) adults that emerged from eggs of Megamelus scutellaris (Hemiptera: Delphacidae), a biological control agent of waterhyacinth, Eichhornia crassipes. Female (lef) and male (right). Photos taken by Jeremiah Foley, USDA-ARS Invasive Plant Research Laboratory.
Fig. 3 in Host range of the invasive tomato pest Tuta absoluta Meyrick (Lepidoptera: Gelechiidae) on solanaceous crops and weeds in Tanzania
Fig. 3. Tuta absoluta-related damage in solanaceous crops and weeds in Tanzania. Damage values are averaged across all survey locations (see Table 1). Damage was quantified as the number of T. absoluta mines per leaf (A) and percentage of T. absoluta-damaged fruits (B) in 10 locations within each sampled field. Six to 12 fields were sampled per crop, and 1 to 3 fields per weed species (see Table 2). Tomato, Solanum lycopersicum; eggplant or aubergine, Solanum melongena; African (Afr.) eggplant, Solanum aethiopicum; African (Afr.) nightshades, Solanum nigrum and Solanum americanum; pepper, Capsicum annuum; and 3 weed species, Datura stramonium, Nicandra physalodes, and Solanum incanum.
Fig. 1 in Host range of the invasive tomato pest Tuta absoluta Meyrick (Lepidoptera: Gelechiidae) on solanaceous crops and weeds in Tanzania
Fig. 1. Map of locations surveyed to determine host range and infestation level of Tuta absoluta in solanaceous crops and weeds in 2015. Four districts were targeted, each within the major tomato-producing regions of Tanzania. Within each district, 3 villages (indicated by black circles) were randomly selected for the survey (see Table 1).
Fig. 2 in Survival and development of fall armyworm (Lepidoptera: Noctuidae) in weeds during the off-season
Fig. 2. Insect survival (mean ± SE) (a), biomass (mean ± SE) (b), and injury level (mean ± SE) (c) of Spodoptera frugiperda fed with 6 weeds and maize in the greenhouse for 21 d. Means capped with the same letter do not differ significantly.
Fig. 1 in Survival and development of fall armyworm (Lepidoptera: Noctuidae) in weeds during the off-season
Fig. 1. Mean (± EP) of larval survival and pre-imaginal survival (%) (a), larval development time and pre-imaginal development time (b), and biomass of larvae and pupae of surviving individuals of Spodopterafrugiperda fed with 6 weeds and maize in laboratory conditions (c). Uppercase letters are used to compare larval stage, while lowercase letters are used to compare pupal stage. Means capped with the same letter do not differ significantly. Means followed by an asterisk (*) were zero (0), and were not used in the analysis.
Figure 1 Aceria alhagi n in A new Aceria species (Acari:Trombidiformes: Eriophyoidea) from West Asia, a potential biological control agent for the invasive weed camelthorn, Alhagi maurorum Medik. (Leguminosae)
Figure 1 Aceria alhagi n.sp.: AD – Antero-dorsal mite; AL – Antero-lateral view of mite; CG – Coxigenital region of female; em – Empodium; GM – Genital region of male; IG – Internal female genitalia; L1 – Leg I of female; LO – Lateral opisthosoma; PM – Postero-lateral mite. Scale bar: 20μm for AD, AL,CG, GM, IG, LO, PM; 10μm for L1; 5μm for em.
Figure 2 SEM images ofAceria alhagi n in A new Aceria species (Acari:Trombidiformes: Eriophyoidea) from West Asia, a potential biological control agent for the invasive weed camelthorn, Alhagi maurorum Medik. (Leguminosae)
Figure 2 SEM images ofAceria alhagi n. sp.: A – prodorsal shield; B – tarsal empodia on legs I and II; C – ventral view of coxigenital area of female; D – ventral view of coxigenital area of male.
A phenotyping weeds image dataset for open scientific research
<p>This in-house-built image dataset consists of 10810 weed images captured through a dedicated phenotyping activity in quasi-field conditions. The targets are seven of the most widespread and hard-to-control weeds in wheat (but also in other winter cereals) in the Mediterranean environment.</p> <p>In the framework of open scientific research, our aim is to share low-cost and high-resolution images representing challenging agricultural environments where weather, lighting and other factors can change by the hour and affect the quality of images. This way the dataset could be used to train Artificial Intelligence architectures designed for weed recognition, allowing the implementation of tools directly available in the field for farmers and technicians for effective and timely weed management.</p> <p>The dataset encompasses weed images ranging from the post-emergence phase (i.e. the complete cotyledons unfolding) until the pre-flowering stage. The weed selection was made by considering (i) bottom-up information and specific requests by farmers and technicians, (ii) weed susceptibility to commercial formulations for chemical control <50%, reported at least twice by field technicians, (iii) the difficulty of control considering any methods, and (iv) the type of growing season (overlapping or not with wheat). The final weeds selection encompassed both monocots (<em>Avena sterilis</em> and <em>Lolium multiflorum</em>) and dicots (<em>Convolvulus arvensis</em>, <em>Fumaria officinalis</em>, <em>Papaver rhoeas</em>, <em>Veronica persica</em> and <em>Vicia sativa</em>).</p> <p>Image acquisition was facilitated by using a white panel as a background; this helped to (i) spread the light and thereby make the plants well-illuminated, while still avoiding strong shadows when using the flash and (ii) simplify image processing. The images were acquired with a Canon EOS 700D hand-held camera set in the macro mode with aperture, shutter speed, ISO and flash in auto mode. Photo capture timing, target distances and light conditions did not have a fixed pattern but were deliberately programmed to vary in such a way as to mimic field conditions. For image shooting at various times of the day, the only precaution was to frame the subject with homogeneous light conditions (full sunlight/full shade). The varied outdoor conditions (light, distance, timing) and camera type (RGB) with auto mode were essential features to make the images photos look similar to those that a user can take in a field, for example with a smartphone camera.</p> <p>After selection and categorization, images were cropped to select the region of interest following the 1:1 ratio but maintaining a minimum size of 512 x 512 pixels.</p> <p> </p> <p>More details on the dataset and its use for weed recognition tasks will be soon available in the proceedings of the forthcoming ECPA conference (2-6 July 2023, Bologna, Italy).</p>
Dataset and code supporting Cornelis et al. 2023. Stuck in the weeds: Invasive grasses reduce tiger snake movement
<p>Data and code used in the publication:</p> <p>Cornelis, J., Cooper, C. E., Lettoof, D. C., Mayer, M., Marshall, B. M. 2023 Stuck in the weeds: Invasive grasses reduce tiger snake movement. bioRxiv 2023.03.06.531246; doi: https://doi.org/10.1101/2023.03.06.531246</p> <p>Includes telemetry data, aKDE, dBBMM, and Bayesian model specification and results, code to reproduce analysis and generate figures </p> <p> </p> <div> </div>
DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning
<p>DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning</p> <p>This repository makes available the source code and public dataset for the work, "DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning", published with open access by Scientific Reports: <a href="https://www.nature.com/articles/s41598-018-38343-3">https://www.nature.com/articles/s41598-018-38343-3</a>. The DeepWeeds dataset consists of 17,509 images capturing eight different weed species native to Australia in situ with neighbouring flora. In our work, the dataset was classified to an average accuracy of 95.7% with the ResNet50 deep convolutional neural network.</p> <p>The source code, images and annotations are licensed under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> license. The contents of this repository are released under an <a href="https://github.com/AlexOlsen/DeepWeeds/blob/master/LICENSE">Apache 2</a> license.</p> <p> </p> <p>Download the dataset images and our trained models</p> <ul> <li>images.zip (468 MB)</li> <li>models.zip (477 MB)</li> </ul> <p>Due to the size of the images and models they are hosted outside of the Github repository. The images and models must be downloaded into directories named "images" and "models", respectively, at the root of the repository. If you execute the python script (deepweeds.py), as instructed below, this step will be performed for you automatically.</p> <p> </p> <p><strong>TensorFlow Datasets</strong></p> <p>Alternatively, you can access the DeepWeeds dataset with <a href="https://www.tensorflow.org/datasets">TensorFlow Datasets</a>, TensorFlow's official collection of ready-to-use datasets. <a href="https://www.tensorflow.org/datasets/catalog/deep_weeds">DeepWeeds</a> was officially added to the TensorFlow Datasets catalog in August 2019.</p> <p> </p> <p><strong>Weeds and locations</strong></p> <p>The selected weed species are local to pastoral grasslands across the state of Queensland. They include: "Chinee apple", "Snake weed", "Lantana", "Prickly acacia", "Siam weed", "Parthenium", "Rubber vine" and "Parkinsonia". The images were collected from weed infestations at the following sites across Queensland: "Black River", "Charters Towers", "Cluden", "Douglas", "Hervey Range", "Kelso", "McKinlay" and "Paluma". The table and figure below break down the dataset by weed, location and geographical distribution.</p> <p> </p> <p> </p><p><strong>Data organization</strong></p> <p></p> <p>Images are assigned unique filenames that include the date/time the image was photographed and an ID number for the instrument which produced the image. The format is like so: <code>YYYYMMDD-HHMMSS-ID</code>, where the ID is simply an integer from 0 to 3. The unique filenames are strings of 17 characters, such as 20170320-093423-1.</p> <p> </p> <p><strong>labels</strong></p> <p>The labels.csv file assigns species labels to each image. It is a comma separated text file in the format:</p> <pre><code>Filename,Label,Species ... 20170207-154924-0,jpg,7,Snake weed 20170610-123859-1.jpg,1,Lantana 20180119-105722-1.jpg,8,Negative ... </code></pre> <p><em>Note: The specific label subsets of training (60%), validation (20%) and testing (20%) for the five-fold cross validation used in the paper are also provided here as CSV files in the same format as "labels.csv".</em></p> <p> </p> <p><strong>models</strong></p> <p>We provide the most successful ResNet50 and InceptionV3 models saved in Keras' hdf5 model format. The ResNet50 model, which provided the best results, has also been converted to UFF format in order to construct a TensorRT inference engine.</p> <pre><code>resnet.hdf5 inception.hdf5 resnet.uff </code></pre> <p> </p> <p><strong>deepweeds.py</strong></p> <p>This python script trains and evaluates Keras' base implementation of ResNet50 and InceptionV3 on the DeepWeeds dataset, pre-trained with ImageNet weights. The performance of the networks are cross validated for 5 folds. The final classification accuracy is taken to be the average across the five folds. Similarly, the final confusion matrix from the associated paper aggregates across the five independent folds. The script also provides the ability to measure the inference speeds within the TensorFlow environment.</p> <p>The script can be executed to carry out these computations using the following commands.</p> <ul> <li>To train and evaluate the ResNet50 model with five-fold cross validation, use <code>python3 deepweeds.py cross_validate --model resnet</code>.</li> <li>To train and evaluate the InceptionV3 model with five-fold cross validation, use <code>python3 deepweeds.py cross_validate --model inception</code>.</li> <li>To measure inference times for the ResNet50 model, use <code>python3 deepweeds.py inference --model models/resnet.hdf5</code>.</li> <li>To measure inference times for the InceptionV3 model, use <code>python3 deepweeds.py inference --model models/inception.hdf5</code>.</li> </ul> <p> </p> <p><strong>Dependencies</strong></p> <p>The required Python packages to execute deepweeds.py are listed in requirements.txt.</p> <p> </p> <p><strong>tensorrt</strong></p> <p>This folder includes C++ source code for creating and executing a ResNet50 TensorRT inference engine on an NVIDIA Jetson TX2 platform. To build and run on your Jetson TX2, execute the following commands:</p> <pre><code>cd tensorrt/src make -j4 cd ../bin ./resnet_inference </code></pre> <p> </p> <p><strong>Citations</strong></p> <p>If you use the DeepWeeds dataset in your work, please cite it as:</p> <p>IEEE style citation: “A. Olsen, D. A. Konovalov, B. Philippa, P. Ridd, J. C. Wood, J. Johns, W. Banks, B. Girgenti, O. Kenny, J. Whinney, B. Calvert, M. Rahimi Azghadi, and R. D. White, “DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning,” <em>Scientific Reports</em>, vol. 9, no. 2058, <strong>2</strong> 2019. [Online]. Available: <a href="https://doi.org/10.1038/s41598-018-38343-3">https://doi.org/10.1038/s41598-018-38343-3</a> ”</p> <p> </p> <p><strong>BibTeX</strong></p> <pre><code>@article{DeepWeeds2019, author = {Alex Olsen and Dmitry A. Konovalov and Bronson Philippa and Peter Ridd and Jake C. Wood and Jamie Johns and Wesley Banks and Benjamin Girgenti and Owen Kenny and James Whinney and Brendan Calvert and Mostafa {Rahimi Azghadi} and Ronald D. White}, title = {{DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning}}, journal = {Scientific Reports}, year = 2019, number = 2058, month = 2, volume = 9, issue = 1, day = 14, url = "https://doi.org/10.1038/s41598-018-38343-3", doi = "10.1038/s41598-018-38343-3" } </code></pre>
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>
Host-associated genetic differentiation and origin of a recent host shift in the generalist parasitic weed Phelipanche ramosa
<p>The branched broomrape, <em>Phelipanche ramosa</em> (L.) Pomel, is a parasitic weed that can infest several crops, notably tobacco, hemp and tomato. In western France, it has recently adapted to a new host, oilseed rape. We collected <em>P. ramosa</em> samples from fields cultivated with six different crops across Europe. Data from SSR markers and DNA sequences showed strong host-associated genetic differentiation.</p> <p>File SSRdata-Pramosa1611.txt contains sampling locations, host crops and microsatellite genotypes.</p> <p>Files BO1aligned.fas, ITSaligned.fas, RPL16aligned.fas and trnKtrnQaligned.fas contain aligned DNA sequences.</p>
Figure 1 in Oviposition of Quesada gigas in weed no hostess: implication in pest management
Figure 1 - Oviposition of Quesada gigas in weed Conyza spp. A. Eggs nests into side branch B. Eggs with fusiform format and white milky color.
G-matrix stability of clinally diverging populations of an annual weed
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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.
Non-native weed reaches community dominance under the canopy of native tree
Whether facilitation from native plants is strong enough to trigger community dominance by non-natives remains unclear. We explored the possibility that facilitation from Prosopis caldenia, the dominant native tree in the semiarid open forest of central Argentina, drives local community dominance by Chenopodium album, an annual herb native to Europe. We assessed this hypothesis by conducting extensive field sampling in which we recorded the relative abundance of species growing under the canopy of P. caldenia (caldén microsites) and in adjacent locations free of this tree (open microsites). If our hypothesis is correct, then the relative abundance of C. album will be greater than that of the rest of the species only when growing under P. caldenia. Also, we measured C. album performance, estimated its soil seed bank, and characterized growing conditions in caldén and open microsites. We found that the relative abundance of C. album was over seven times greater than that of any other species in communities occurring in caldén microsites; by contrast, C. album co-dominated communities with several other species in the open. Chenopodium album density, cover, biomass, and fecundity were all several times greater in caldén than open microsites. Similarly, C. album seed bank displayed an eight-fold increase in caldén as compared to open microsites. Growing conditions were markedly different between microsites, which could explain positive responses from C. album. Our results suggest that facilitation from natives is indeed strong enough to trigger local community dominance by non-natives, advancing the understanding of community-level consequences of this interaction.
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DANDI Archive for NWB datasets
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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.