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  1. R

    Plant Diseases Dataset

    • universe.roboflow.com
    zip
    Updated Nov 15, 2022
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    CSUEB (2022). Plant Diseases Dataset [Dataset]. https://universe.roboflow.com/csueb/plant-diseases-c2yf6
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 15, 2022
    Dataset authored and provided by
    CSUEB
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Diseases Bounding Boxes
    Description

    Here are a few use cases for this project:

    1. Agricultural Health Monitoring: The "Plant-diseases" model can be used by farmers to monitor the health status of their crops in real time. Identifying diseases early can help apply preventative measures promptly, significantly improving crop yield.

    2. Digital Farming Apps: This model can be incorporated into agricultural mobile applications to help farmers identify plant diseases by simply taking a picture of their crops. The app can provide instant diagnosis and treatment recommendations.

    3. Research and Development: The "Plant-diseases" model can provide valuable data for agricultural researchers studying crop diseases. By using the model, researchers can quickly categorize different types of plant diseases and identify patterns and correlations.

    4. Smart Farming Equipment: This model can be integrated into smart farming equipment such as drones or robotics for crop surveillance. Such equipment can scan large areas of field quickly, identifying unhealthy plants and alerting farmers about potential disease outbreaks.

    5. Educational Tool: Institutions teaching agriculture or botany-related subjects can use this model as a practical tool for educating students about plant diseases. They can easily show what different types of diseases look like and explain how they affect plants.

  2. New Plant Diseases Dataset

    • kaggle.com
    Updated Nov 24, 2018
    + more versions
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    Samir Bhattarai (2018). New Plant Diseases Dataset [Dataset]. https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 24, 2018
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Samir Bhattarai
    Description

    This dataset is recreated using offline augmentation from the original dataset. The original dataset can be found on this github repo. This dataset consists of about 87K rgb images of healthy and diseased crop leaves which is categorized into 38 different classes. The total dataset is divided into 80/20 ratio of training and validation set preserving the directory structure. A new directory containing 33 test images is created later for prediction purpose.

  3. D

    DiaMOS Plant Dataset

    • datasetninja.com
    Updated Oct 8, 2021
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    Gianni Fenu; Francesca Maridina Malloci (2021). DiaMOS Plant Dataset [Dataset]. https://datasetninja.com/diamos-plant
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    Dataset updated
    Oct 8, 2021
    Dataset provided by
    Dataset Ninja
    Authors
    Gianni Fenu; Francesca Maridina Malloci
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The authors of the DiaMOS Plant: A Dataset for Diagnosis and Monitoring Plant Disease contribute to the evolving field of foliar disease classification and recognition through the utilization of machine and deep learning concepts. It has 3505 images of pear fruit and leaves affected by four diseases: slug leaf, spot leaf, curl leaf, and healthy leaf. The study offers valuable guidelines for the research community to select and construct further datasets.

  4. One-hundred plant species leaves

    • kaggle.com
    Updated May 5, 2023
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    Utkarsh Singh (2023). One-hundred plant species leaves [Dataset]. https://www.kaggle.com/datasets/utkarshx27/one-hundred-plant-species-leaves
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 5, 2023
    Dataset provided by
    Kaggle
    Authors
    Utkarsh Singh
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Data Set Information:

    For Each feature, a 64 element vector is given per sample of leaf. These vectors are taken as a contigous descriptors (for shape) or histograms (for texture and margin).

    Attribute Information:

    For Each feature, a 64 element vector is given per sample of leaf. One file for each 64-element feature vectors. Each row begins with the class label. The remaining 64 elements is the feature vector.

    Relevant Papers:

    This is a new data set, provisional paper: 'Plant Leaf Classification Using

    Probabilistic Integration of Shape, Texture and Margin Features' at SPPRA 2013. Authors: Charles Mallah, James Cope, and James Orwell or Kingston University London.

    Previous parts of the data set relate to feature extraction of leaves from: J. Cope, P. Remagnino, S. Barman, and P. Wilkin. Plant texture classification using gabor cooccurrences. Advances in Visual Computing, pages 669–677, 2010.

    T. Beghin, J. Cope, P. Remagnino, and S. Barman. Shape and texture based plant leaf classification. In Advanced Concepts for Intelligent Vision Systems, pages 345–353. Springer, 2010.

    Source:

    James Cope, Thibaut Beghin, Paolo Remagnino, Sarah Barman. The colour images are not included in this submission. The Leaves were collected in the Royal Botanic Gardens, Kew, UK. email: james.cope '@' kingston.ac.uk

    This dataset consists of work carried out by James Cope, Charles Mallah, and James Orwell. Kingston University London. Donor of database Charles Mallah: charles.mallah '@' kingston.ac.uk; James Cope: james.cope '@' kingston.ac.uk

  5. m

    MED117_Medicinal Plant Leaf Dataset & Name Table

    • data.mendeley.com
    Updated Jan 19, 2023
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    Parismita Sarma (2023). MED117_Medicinal Plant Leaf Dataset & Name Table [Dataset]. http://doi.org/10.17632/dtvbwrhznz.4
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    Dataset updated
    Jan 19, 2023
    Authors
    Parismita Sarma
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    There are two datasets and one table uploaded in this platform under the title "MED117_Medicinal Plant Leaf Dataset & Name Table". A folder is created with title "MED 117 Leaf Species". Inside this two sub folders with titles " Raw leaf image set of medicinal plants_v2" and "Segmented leaf set using UNET segmentation" are created. Raw leaf image set consists of leaf images of 117 medicinal plants found in Assam. All the samples are collected by visiting different (Govt, Public and Private) medicinal gardens situated in different places of Assam and some other general places where they are mostly found. Videos of 10 to 15 seconds duration were taken for two to three leaves of every species on a white background and video recording was done using a SLR Canon Camera. Individual videos were segregated into image frames and thus were able to get around 77,700 jpg image frames from the videos. The Raw leaf image set consists of folders with scientific name and common name within bracket. Second folder with title "Segmented leaf set using UNET segmentation" consists of 115 medicinal plant species with their segmented leaf image samples using UNET segmentation technique. Here two species are excluded from the original dataset due to small unpredictable size of the samples, so total 115 subfolders inside the segmented folder is achieved. Thirdly a table in doc format with title "Medicinal Plant Name Table" is uploaded and it includes Scientific name, Common name and Assamese name of the plants listed in the folders in the same sequence. The whole contribution is absolutely original and new, collected from different sources then processed for segmentation and prepared the table by discussing with taxonomy experts from Botany department of Gauhati University, Guwahati, Assam. India.

  6. a

    PlantVillage

    • datasets.activeloop.ai
    • tensorflow.org
    • +2more
    deeplake
    Updated Feb 3, 2022
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    Arun Pandian J, Geetharamani Gopal (2022). PlantVillage [Dataset]. https://datasets.activeloop.ai/docs/ml/datasets/plantvillage-dataset/
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    deeplakeAvailable download formats
    Dataset updated
    Feb 3, 2022
    Authors
    Arun Pandian J, Geetharamani Gopal
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    A dataset of 61,486 images of plant leaves and backgrounds, with each image labeled with the disease or pest that is present. The dataset was created by researchers at the University of Wisconsin-Madison and is used for research in machine learning and computer vision tasks such as plant disease detection and pest identification.

  7. R

    Medicinal Plant Dataset

    • universe.roboflow.com
    zip
    Updated Aug 18, 2024
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    Vietnam Medicinal Plants (2024). Medicinal Plant Dataset [Dataset]. https://universe.roboflow.com/vietnam-medicinal-plants/medicinal-plant-63aij
    Explore at:
    zipAvailable download formats
    Dataset updated
    Aug 18, 2024
    Dataset authored and provided by
    Vietnam Medicinal Plants
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Medi Bounding Boxes
    Description

    Medicinal Plant

    ## Overview
    
    Medicinal Plant is a dataset for object detection tasks - it contains Medi annotations for 1,058 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
    
  8. Yolov(8,10)_format_leaf_disease_dataset

    • kaggle.com
    Updated Jun 15, 2024
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    Deepak NR (2024). Yolov(8,10)_format_leaf_disease_dataset [Dataset]. https://www.kaggle.com/datasets/deepaknr/yolov10-leaf-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 15, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Deepak NR
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    Dataset Overview: The Leaf Disease Dataset is a comprehensive collection of images designed for the classification and detection of various leaf diseases. This dataset is ideal for training and evaluating machine learning models in the field of plant pathology, agricultural research, and computer vision.

    Classes: The dataset consists of images categorized into three distinct classes representing different types of leaf diseases:

    Mildew:

    Description: Mildew is a fungal disease that affects a wide range of plants, characterized by a white, powdery coating on the surface of the leaves. It can cause significant damage to crops by interfering with photosynthesis and reducing plant vigor. Visual Features: White or gray powdery spots on the leaves, which may spread and cover large areas as the infection progresses. Rose_P01:

    Description: This class represents a specific type of disease affecting rose plants. It is designated as 'Rose_P01' and includes various symptoms that are detrimental to the health of rose plants. Visual Features: Symptoms may include discolored spots, lesions, or patches on the leaves, which can lead to reduced blooming and overall plant health. Rose_R02:

    Description: Similar to 'Rose_P01', the 'Rose_R02' class represents another disease variant affecting rose plants. This class includes different symptoms and manifestations specific to this disease type. Visual Features: Symptoms may vary from spots and lesions to more severe signs such as leaf curling, yellowing, or premature leaf drop, indicating a different disease pathology compared to 'Rose_P01'. Dataset Specifications:

    Image Format: The images in this dataset are in high-resolution JPEG format. Image Size: Each image is standardized to a consistent size to ensure uniformity across the dataset. Annotations: Each image is labeled with its corresponding class, facilitating supervised learning tasks. Applications:

    Disease Detection and Classification: This dataset can be used to train machine learning models to accurately detect and classify leaf diseases in real-time. Agricultural Research: Researchers can utilize this dataset to study the spread and impact of different leaf diseases on crop health. Precision Agriculture: The dataset supports the development of automated systems for early disease detection, helping farmers take proactive measures to protect their crops. Usage:

    Training and Testing: Suitable for splitting into training and testing subsets for machine learning experiments. Model Benchmarking: Can be used to benchmark the performance of different algorithms in leaf disease classification tasks. Conclusion: The Leaf Disease Dataset provides a valuable resource for the development of robust and accurate models for disease detection in plants. By leveraging this dataset, researchers and practitioners can advance the field of plant pathology and contribute to more sustainable agricultural practices.

  9. n

    NEON (National Ecological Observatory Network) Plant presence and percent...

    • data.neonscience.org
    zip
    + more versions
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    NEON (National Ecological Observatory Network) Plant presence and percent cover (DP1.10058.001) [Dataset]. https://data.neonscience.org/data-products/DP1.10058.001
    Explore at:
    zipAvailable download formats
    License

    https://www.neonscience.org/data-samples/data-policies-citationhttps://www.neonscience.org/data-samples/data-policies-citation

    Time period covered
    Jun 2013 - Nov 2024
    Area covered
    Description

    Plant species cover-abundance and presence observed in multi-scale plots. Plant species and associated percent cover in 1m2 subplots and plant species presence in 10m2 and 100m2 subplots are reported from 400m2 plots. Archived plant vouchers and foliar tissue support the data and additional analyses.

  10. h

    plant-kaggle-seg-data

    • huggingface.co
    Updated May 29, 2024
    + more versions
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    plant-kaggle-seg-data [Dataset]. https://huggingface.co/datasets/Juliekyungyoon/plant-kaggle-seg-data
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 29, 2024
    Authors
    Jung
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    Juliekyungyoon/plant-kaggle-seg-data dataset hosted on Hugging Face and contributed by the HF Datasets community

  11. Geohistorical plants occurrences database

    • gbif.org
    Updated Oct 27, 2022
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    Morgane Claudel; Emilie Lerigoleur; Cécile Brun; Sylvie Guillerme; Morgane Claudel; Emilie Lerigoleur; Cécile Brun; Sylvie Guillerme (2022). Geohistorical plants occurrences database [Dataset]. http://doi.org/10.15468/3kvaeh
    Explore at:
    Dataset updated
    Oct 27, 2022
    Dataset provided by
    Global Biodiversity Information Facilityhttps://www.gbif.org/
    UMR 5602 GEODE Géographie de l’environnement (CNRS/Université Toulouse 2)
    Authors
    Morgane Claudel; Emilie Lerigoleur; Cécile Brun; Sylvie Guillerme; Morgane Claudel; Emilie Lerigoleur; Cécile Brun; Sylvie Guillerme
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1651 - Dec 31, 2004
    Area covered
    Description

    The data file contains occurrence data based on historical observations and records between 1651 and 2004. Ten plant species have been studied : Alnus incana (L.) Moench, 1794 ; Buddleja davidii Franch., 1887 ; Castanea sativa Mill., 1798 ; Helianthus tuberosus L., 1753 ; Impatiens glandulifera Royle, 1833 ; Prunus cerasifera Ehrh., 1784 ; Prunus laurocerasus L., 1753 ; Reynoutria japonica Houtt., 1777 ; Robinia pseudoacacia L., 1753 ; and Spiraea japonica L.f., 1782. The data file is the result of a geo-historical study conducted over five months on the invasive plants species's introduction and distribution in Occitania (France), carried out within the framework of the EI2P-VALEEBEE project (Invasive species and pollinators, between constraints and potentials). Historical sources have been consulted during 2020 in order to find the oldest elements about the ten species. Each data corresponds to an historical observation or mention on one of the ten species of the study mainly on Metropolitan French territory since their introduction. Without an historical analysis, it is difficult to understand the current local distribution dynamics of invasive plant species, especially when some of them have been introduced on Metropolitan French territory for several centuries. All the interest of these occurrence data is to bring an historical depth allowing us to apprehend the local distribution of the ten species of the study over time. This can be allowed thanks to the record of several elements on their places of introduction, the comments from authors and observers on their abundance, and elements on the historical context of introduction. More generally, this historical data file is part of a multidisciplinary approach proposed by the members of EI2P project whose objective is to better take into account the ecological socio-cultural and economic issues raised by the issue of invasive alien plants.

    This work was endorsed by the CNRS/INEE Zone Atelier Pyrénées Garonne (ZA PYGAR). The Zones Ateliers network (RZA) is recognized by ALLENVI, as an eLTER (European Long-Term Ecological Research).

    A data paper explains precisely this dataset: Claudel M, Lerigoleur E, Brun C, Guillerme S (2022) Geohistorical dataset of ten plant species introduced into Occitania (France). Biodiversity Data Journal 10: e76283. https://doi.org/10.3897/BDJ.10.e76283

  12. Plant Disease Classification Merged Dataset

    • kaggle.com
    Updated Mar 3, 2023
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    Aline Dobrovsky (2023). Plant Disease Classification Merged Dataset [Dataset]. https://www.kaggle.com/datasets/alinedobrovsky/plant-disease-classification-merged-dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 3, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Aline Dobrovsky
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    This dataset was created as part of a study project on plant disease classification. The self-imposed requirements for the dataset were as follows: large amount of images, for each plant at least healthy and one disease, most common diseases, annotated images, laboratory and field images, important staple food and highest global production plant species. Because of the requirements towards the dataset, no existing dataset could be used, but a new dataset was compiled by combining images from 14 different existing datasets. All included datasets, their properties and the imported images are listed in the table below along with the proper references.. This new dataset contains laboratory as well as field images. Images of non-food plants, singular condition classes, watermarked images and classes with less than 50 examples were removed. The final dataset used for the training within the project contains 88 classes with above 76,000 images of overall size of 17.6GB. What can not be avoided in general is a bias among the classes, since in some cases different shooting conditions were used for the images (e.g. classes with mainly laboratory images or different soil in the background).

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11839378%2Fae7dbbbf4e96ec87f8055303a618b24c%2FKaggleTableData.png?generation=1677868803290050&alt=media" alt="">

    [1] F. Nahian, “Plant disease [65 classes],” kaggle dataset: https://www.kaggle.com/datasets/fabinahian/plant-disease-65-classes, 09 2022, accessed: 26.12.2022. [2] D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, and N. Batra, “Plantdoc: A dataset for visual plant disease detection,” in Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, ser. CoDS COMAD 2020. New York, NY, USA: Association for Computing Machinery, 2020, p. 249–253. doi: 10.1145/3371158.3371196. ISBN 9781450377386. [Online]. Available: https://doi.org/10.1145/3371158.3371196 [3] C. Disease, “Coffee plant disease,” kaggle dataset: https://www.kaggle.com/datasets/coffeedisease/coffee-plant-disease, 2021, last accessed: 23.02.2023. [4] O. Getch, “Wheat leaf dataset,” kaggle dataset: https://www.kaggle.com/datasets/olyadgetch/wheat-leaf-dataset, 2021, last accessed: 23.02.2023. [5] D. D. Prakoso, “Chili plant disease,” kaggle dataset: https://www.kaggle.com/datasets/dhenyd/chili-plant-disease, 2021, last accessed: 23.02.2023. [6] M. E. Mignoni, “Images of soybean leaves,” mendeley dataset: https://data.mendeley.com/datasets/bycbh73438/1, December 2021, last accessed: 23.02.2023. [7] D. V. Prajapati HB, Shah JP, “Rice leaf diseases dataset (detection and classification of rice plant diseases. intelligent decision technologies.),” kaggle dataset: https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases, pp. 357–73, January 2017. [8] S. Riyaz, “Rice leafs,” kaggle dataset: https://www.kaggle.com/datasets/shayanriyaz/riceleafs, 2019, last accessed: 23.02.2023. [9] K. Negm, “Cucumber plant diseases dataset,” kaggle dataset: https://www.kaggle.com/datasets/kareem3egm/cucumber-plant-diseases-dataset, 2021, last accessed: 23.02.2023. [10] S. S. Mahi, “Plant disease expert,” kaggle dataset: https://www.kaggle.com/datasets/sadmansakibmahi/plant-disease-expert, 12 2022, accessed: 26.12.2022. [11] H. A. Iranga, “Leaf disease dataset (combination),” kaggle dataset: https://www.kaggle.com/datasets/asheniranga/leaf-disease-dataset-combination, 2022, last accessed: 23.02.2023. [12] L. V. S. T. T. BARBEDO, J. G. A.; KOENIGKAN, “Identifying multiple plant diseases using digital image processing,” Biosystems Engineering, vol. 147, pp. 104–116, 07 2016. [13] P. Soundar, “Sugarcane disease dataset,” kaggle dataset: https://www.kaggle.com/datasets/prabhakaransoundar/sugarcane-disease-dataset, 2022, last accessed: 23.02.2023. [14] R. B. A. R. Pungliya Vithika, Atharva Purohit, “Sugarcane leaf disease classification,” kaggle dataset: https://www.kaggle.com/datasets/pungliyavithika/sugarcaneleaf-disease-classification, 2022, last accessed: 23.02.2023.

  13. D

    PlantDoc Dataset

    • datasetninja.com
    Updated Oct 16, 2019
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    Singh, Davinder; Jain, Naman; Jain, Pranjali (2019). PlantDoc Dataset [Dataset]. https://datasetninja.com/plantdoc
    Explore at:
    Dataset updated
    Oct 16, 2019
    Dataset provided by
    Dataset Ninja
    Authors
    Singh, Davinder; Jain, Naman; Jain, Pranjali
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The authors of the PlantDoc: A Dataset for Visual Plant Disease Detection recognized the importance of training models with real-life images to account for the complexities of the real world. In light of this, they decided to create a dataset for accurate plant disease detection in the farm setting by downloading images from Google Images and Ecosia. Collecting large-scale plant disease data through fieldwork would have required significant effort, so they gathered approximately 20,900 images making the final dataset having a total of 27 classes spanning over 13 species with 2,598 images. Numbers in the claimed and the actual dataset differ.

  14. The China Plant Trait Database Version 2.0

    • figshare.com
    txt
    Updated Sep 14, 2023
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    Han Wang; S P Harrison; Meng Li; Iain-Colin Prentice; Shengchao Qiao; Runxi Wang; Huiying Xu; Giulia Mengoli; Yunke Peng; Yanzheng Yang (2023). The China Plant Trait Database Version 2.0 [Dataset]. http://doi.org/10.6084/m9.figshare.19448219.v7
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    txtAvailable download formats
    Dataset updated
    Sep 14, 2023
    Dataset provided by
    figshare
    Authors
    Han Wang; S P Harrison; Meng Li; Iain-Colin Prentice; Shengchao Qiao; Runxi Wang; Huiying Xu; Giulia Mengoli; Yunke Peng; Yanzheng Yang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Here we present version 2.0 of the China Plant Trait Database, which contains information on morphometric, physical, chemical, photosynthetic and hydraulic traits from 1529 unique species in 140 sites spanning a diversity of vegetation types. Version 2 has five improvements compared to the previous version: (1) new data from a 4-km elevation transect on the edge of Tibetan Plateau, including alpine vegetation types not sampled previously; (2) inclusion of traits related to hydraulic processes, including specific sapwood conductance, the area ratio of sapwood to leaf, wood density and leaf turgor loss point; (3) inclusion of information on soil properties to complement the existing data on climate and vegetation (4) assessments of the reliability of individual trait measurements; and (5) inclusion of standardized checklists and templates for systematical field sampling and measurements. See detailed descriptions here: Wang, H., Harrison, S.P., Li, M. et al. The China plant trait database version 2. Sci Data 9, 769 (2022). https://doi.org/10.1038/s41597-022-01884-4

  15. R

    Plants Diseases Detection And Classification Dataset

    • universe.roboflow.com
    zip
    Updated Aug 4, 2023
    + more versions
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    Graduation Project 2023 (2023). Plants Diseases Detection And Classification Dataset [Dataset]. https://universe.roboflow.com/graduation-project-2023/plants-diseases-detection-and-classification
    Explore at:
    zipAvailable download formats
    Dataset updated
    Aug 4, 2023
    Dataset authored and provided by
    Graduation Project 2023
    Variables measured
    Plants Bounding Boxes
    Description

    Plants Diseases Detection And Classification

    ## Overview
    
    Plants Diseases Detection And Classification is a dataset for object detection tasks - it contains Plants annotations for 2,516 images.
    
    ## Getting Started
    
    You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.
    
      ## License
    
      This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).
    
  16. The Himalayan Uplands Plant database (HUP Version 1)

    • gbif.org
    • bionomia.net
    • +7more
    Updated Aug 18, 2016
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    GBIF (2016). The Himalayan Uplands Plant database (HUP Version 1) [Dataset]. http://doi.org/10.15468/k64rgi
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    Dataset updated
    Aug 18, 2016
    Dataset provided by
    Global Biodiversity Information Facilityhttps://www.gbif.org/
    Global Mountain Biodiversity Assessment - GMBA
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Area covered
    Himalayas
    Description

    This unique and huge data set contains plant information for the Himalaya Uplands; it consists of 164,360 records. This database is implemented in MS ACCESS following ABCD 1.2. It describes Asian plant species related to the Tibetan Plateau, Central Asia. Data have been collected for over 50 years, and in over 11 countries (e.g. Afghanistan, Pakistan, Bhutan, China,India, Kazakhstan, Kyrgyztan, Myanmar, Nepal, Russia, Tajikistan, Turkmenistan, Uzbekistan), covering over 220 national regions. Taxonomic information for this region is diverse and not well studied. However, the database follows ICBN taxonomy matched with ITIS and consists of over 5,562 unique species entries. From these, ITIS has 996 species listed. Over 2,200 collectors from all over the world contributed to this dataset, which mostly was compiled and maintained by the author for over 20 years. This database covers 21,869 localities. virtually all sites are georeferenced with latitude and longitude (2 decimals; geographic datum of WGS84), and 6,668 of such unique locations are found in the HUP database. This dataset has altitude information provided by the fieldworker.

  17. O

    Waterwise plants

    • data.qld.gov.au
    • devweb.dga.links.com.au
    • +1more
    csv
    Updated Oct 12, 2022
    + more versions
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    Local Government, Water and Volunteers (2022). Waterwise plants [Dataset]. https://www.data.qld.gov.au/dataset/waterwise-plants
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    csv(1572864)Available download formats
    Dataset updated
    Oct 12, 2022
    Dataset authored and provided by
    Local Government, Water and Volunteers
    License

    Attribution 3.0 (CC BY 3.0)https://creativecommons.org/licenses/by/3.0/
    License information was derived automatically

    Description

    Waterwise plant information. Includes information like botanical names; water, climate, soil and light needs; level of maintenance required etc.

  18. E

    Insect species richness for each plant species and insect-plant interactions...

    • catalogue.ceh.ac.uk
    • data-search.nerc.ac.uk
    • +1more
    zip
    Updated Jul 2, 2020
    + more versions
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    Padovani, R.; Ward, L.; Smith, R.M.; Pocock, M.J.O.; Roy, D.B. (2020). Insect species richness for each plant species and insect-plant interactions from the Database of Insects and their Food Plants [DBIF] version 2 [Dataset]. http://doi.org/10.5285/33a825f3-27cb-4b39-b59c-0f8182e8e2e4
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    zipAvailable download formats
    Dataset updated
    Jul 2, 2020
    Dataset provided by
    NERC EDS Environmental Information Data Centre
    Authors
    Padovani, R.; Ward, L.; Smith, R.M.; Pocock, M.J.O.; Roy, D.B.
    Time period covered
    Jan 1, 1891 - Dec 31, 1988
    Area covered
    Dataset funded by
    Natural Environment Research Council
    Description

    This dataset consists of 4,397 insect species associated with 679 native plant species, 120 archaeophytes, and 234 neophytes from the Database of Insects and their Food Plants (DBIF). The DBIF details approximately 60,000 interactions between phytophagous insect (and mite) species and plants recorded in Great Britain over the last century, based on a wide variety of sources, including entomological journals and field guides. The data here represents a reduced subset of the full DBIF (13,277 interactions), only including interactions resolved to the species level (insect species x associated with host plant species y), records that have been expertly verified as reliable and included in previous large-scale analyses (Ward 1988; Ward & Spalding 1993; Ward et al. 1995; Ward et al. 2003), and records that are certain to have occurred in Great Britain. Any records originating from captive breeding studies are excluded. Finally, only plants with associated phylogenetic data and native status are included. Host plant distribution size is also included, in addition to a quantification of the distinctiveness of the insect communities found on a subset of the non-native plants. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability.

  19. R

    Plant Dataset

    • universe.roboflow.com
    zip
    Updated Apr 18, 2022
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    plant (2022). Plant Dataset [Dataset]. https://universe.roboflow.com/plant-dyujb/plant-kvsno/model/25
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    zipAvailable download formats
    Dataset updated
    Apr 18, 2022
    Dataset authored and provided by
    plant
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    P Lp L Ap Lanp Lan Yplany Bounding Boxes
    Description

    Here are a few use cases for this project:

    1. Agricultural disease and pest monitoring: The "plant" model could be used to monitor crops for early signs of disease or pest infestations by identifying specific leaf conditions, enabling farmers to apply timely treatments and prevent damage to large portions of their crops.

    2. Plant care and maintenance: Gardeners and horticulture enthusiasts could use the "plant" model to diagnose the health of their plants and determine necessary treatment or care methods, leading to better maintenance and improved plant growth.

    3. Botanical research and education: Researchers and educators can use the "plant" model as a teaching tool to help students identify and study various plant leaves and their diseases, promoting a deeper understanding of botany and plant pathology.

    4. Plant identification and biodiversity tracking: Environmental scientists and nature enthusiasts can utilize the "plant" model to identify plant species in a given area. This can contribute to research on biodiversity, conservation efforts, and the tracking of invasive species.

    5. Horticulture industry quality control: The "plant" model could be used by horticulture industry professionals to monitor plant quality and diagnose potential diseases, allowing them to maintain high standards and address issues proactively to keep consumers satisfied with healthy, attractive plants.

  20. D

    Maize Whole Plant Image Dataset

    • datasetninja.com
    Updated Oct 5, 2017
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    Nicolas Brichet; Llorenç Cabrera-Bosquet (2017). Maize Whole Plant Image Dataset [Dataset]. https://datasetninja.com/maize-whole-plant-image-dataset
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    Dataset updated
    Oct 5, 2017
    Dataset provided by
    Dataset Ninja
    Authors
    Nicolas Brichet; Llorenç Cabrera-Bosquet
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The authors of the Maize Whole Plant Image Dataset mentioned the significance of silks in maize, emphasizing their role in pollen collection and grain number determination, particularly under water deficit conditions. They noted that while silk growth is crucial for drought tolerance in maize, phenotyping it efficiently for genetic analyses is challenging.

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CSUEB (2022). Plant Diseases Dataset [Dataset]. https://universe.roboflow.com/csueb/plant-diseases-c2yf6

Plant Diseases Dataset

plant-diseases-c2yf6

plant-diseases-dataset

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zipAvailable download formats
Dataset updated
Nov 15, 2022
Dataset authored and provided by
CSUEB
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Variables measured
Diseases Bounding Boxes
Description

Here are a few use cases for this project:

  1. Agricultural Health Monitoring: The "Plant-diseases" model can be used by farmers to monitor the health status of their crops in real time. Identifying diseases early can help apply preventative measures promptly, significantly improving crop yield.

  2. Digital Farming Apps: This model can be incorporated into agricultural mobile applications to help farmers identify plant diseases by simply taking a picture of their crops. The app can provide instant diagnosis and treatment recommendations.

  3. Research and Development: The "Plant-diseases" model can provide valuable data for agricultural researchers studying crop diseases. By using the model, researchers can quickly categorize different types of plant diseases and identify patterns and correlations.

  4. Smart Farming Equipment: This model can be integrated into smart farming equipment such as drones or robotics for crop surveillance. Such equipment can scan large areas of field quickly, identifying unhealthy plants and alerting farmers about potential disease outbreaks.

  5. Educational Tool: Institutions teaching agriculture or botany-related subjects can use this model as a practical tool for educating students about plant diseases. They can easily show what different types of diseases look like and explain how they affect plants.