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100+ datasets found
  1. T

    plant_leaves

    • tensorflow.org
    Updated Dec 16, 2022
  2. m

    A Database of Leaf Images: Practice towards Plant Conservation with Plant...

    • data.mendeley.com
    Updated Jun 6, 2019
    + more versions
  3. R

    PlantDoc Object Detection Dataset

    • public.roboflow.com
    zip
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  4. Plant Disease Classification Merged Dataset

    • kaggle.com
    zip
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  5. P

    A Dataset of Multispectral Potato Plants Images Dataset

    • paperswithcode.com
    Updated May 8, 2019
    + more versions
  6. m

    Indian Medicinal Leaves Image Datasets

    • data.mendeley.com
    Updated May 5, 2023
    + more versions
  7. m

    MED117_Medicinal Plant Leaf Dataset & Name Table

    • data.mendeley.com
    • b2find.dkrz.de
    • +1more
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    + more versions
  8. P

    New Plant Diseases Dataset Dataset

    • paperswithcode.com
    Updated Jun 28, 2019
  9. P

    PlantDoc Dataset

    • paperswithcode.com
    • opendatalab.com
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    Updated Feb 25, 2021
  10. CotLeaf-1: Cotton Leaf Surface Images dataset 2019-21

    • data.csiro.au
    • researchdata.edu.au
    Updated Jan 25, 2024
  11. PlantifyDr Dataset

    • kaggle.com
    zip
    Updated Feb 16, 2021
  12. R

    Cotton plant disease prediction Dataset

    • universe.roboflow.com
    zip
    Updated Nov 15, 2022
  13. m

    Indonesian Herb Leaf Dataset 3500

    • data.mendeley.com
    Updated Jan 27, 2022
    + more versions
  14. m

    Data from: PlantaeK: A leaf database of native plants of Jammu and Kashmir

    • data.mendeley.com
    Updated Sep 5, 2019
    + more versions
  15. T

    plant_village

    • tensorflow.org
    • opendatalab.com
    • +1more
    Updated Dec 16, 2022
  16. f

    Plant RNA-Image Repository

    • figshare.com
    zip
    Updated Nov 19, 2023
    + more versions
  17. Maize whole plant image dataset

    • zenodo.org
    Updated Jan 24, 2020
  18. D

    Multispectral Potato Plants Images Dataset

    • datasetninja.com
    Updated Oct 3, 2023
  19. m

    Dataset of Tomato Leaves

    • data.mendeley.com
    Updated May 27, 2020
    + more versions
  20. m

    An Image Dataset of Citrus Fruit and Leaves for Detection and Classification...

    • data.mendeley.com
    • narcis.nl
    Updated May 27, 2019
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Siddharth Singh Chouhan (2019). A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology [Dataset]. http://doi.org/10.17632/hb74ynkjcn.1

A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology

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17 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jun 6, 2019
Authors
Siddharth Singh Chouhan
License

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

Description

The relationship between the plants and the environment is multitudinous and complex. They help in nourishing the atmosphere with diverse elements. Plants are also a substantial element in regulating carbon emission and climate change. But in the past, we have destroyed them without hesitation. For the reason that not only we have lost a number of species located in them, but also a severe result has also been encountered in the form of climate change. However, if we choose to give them time and space, plants have an astonishing ability to recover and re-cloth the earth with varied plant and species that we have, so recently, stormed. Therefore, a contribution has been made in this work towards the study of plant leaf for their identification, detection, disease diagnosis, etc. Twelve economically and environmentally beneficial plants named as Mango, Arjun, Alstonia Scholaris, Guava, Bael, Jamun, Jatropha, Pongamia Pinnata, Basil, Pomegranate, Lemon, and Chinar have been selected for this purpose. Leaf images of these plants in healthy and diseased condition have been acquired and alienated among two separate modules.

Principally, the complete set of images have been classified among two classes i.e. healthy and diseased. First, the acquired images are classified and labeled conferring to the plants. The plants were named ranging from P0 to P11. Then the entire dataset has been divided among 22 subject categories ranging from 0000 to 0022. The classes labeled with 0000 to 0011 were marked as a healthy class and ranging from 0012 to 0022 were labeled diseased class. We have collected about 4503 images of which contains 2278 images of healthy leaf and 2225 images of the diseased leaf. All the leaf images were collected from the Shri Mata Vaishno Devi University, Katra. This process has been carried out form the month of March to May in the year 2019. The images are captured in a closed environment. This acquisition process was completely wi-fi enabled. All the images are captured using a Nikon D5300 camera inbuilt with performance timing for shooting JPEG in single shot mode (seconds/frame, max resolution) = 0.58 and for RAW+JPEG = 0.63. The images were in .jpg format captured with 18-55mm lens with sRGB color representation, 24-bit depth, 2 resolution unit, 1000-ISO, and no flash.

Further, we hope that this study can be beneficial for researchers and academicians in developing methods for plant identification, plant classification, plant growth monitoring, leave disease diagnosis, etc. Finally, the anticipated impression is towards a better understanding of the plants to be planted and their suitable management.

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